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"reshape_30", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "scalar_325": {"edges": [{"target": "reshape_30", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "permute_9": {"edges": [{"target": "unbind_9", "dims": "(3, 4, 4, 16, 60)"}], "failed": false, "node_type": "Operation"}, "scalar_326": {"edges": [{"target": "permute_9", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "scalar_327": {"edges": [{"target": "permute_9", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "scalar_328": {"edges": [{"target": "permute_9", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "scalar_329": {"edges": [{"target": "permute_9", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "scalar_330": {"edges": [{"target": "permute_9", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "unbind_9": {"edges": [{"target": "Identity_130", "dims": "(4, 4, 16, 60)"}, {"target": "Identity_131", "dims": "(4, 4, 16, 60)"}, {"target": 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{"target": "__add___20", "dims": "(4, 16, 240)"}], "failed": false, "node_type": "Operation"}, "LayerNorm_20": {"edges": [{"target": "Linear_35", "dims": "(4, 16, 240)"}], "failed": false, "node_type": "Module"}, "Linear_35": {"edges": [{"target": "SiLU_31", "dims": "(4, 16, 480)"}], "failed": false, "node_type": "Module"}, "SiLU_31": {"edges": [{"target": "Dropout_26", "dims": "(4, 16, 480)"}], "failed": false, "node_type": "Module"}, "Dropout_26": {"edges": [{"target": "Identity_134", "dims": "(4, 16, 480)"}], "failed": false, "node_type": "Module"}, "Identity_134": {"edges": [{"target": "Linear_36", "dims": "(4, 16, 480)"}], "failed": false, "node_type": "Module"}, "Linear_36": {"edges": [{"target": "Dropout_27", "dims": "(4, 16, 240)"}], "failed": false, "node_type": "Module"}, "Dropout_27": {"edges": [{"target": "Identity_135", "dims": "(4, 16, 240)"}], "failed": false, "node_type": "Module"}, "Identity_135": {"edges": [{"target": "Identity_136", "dims": "(4, 16, 240)"}], 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{"edges": [{"target": "reshape_33", "dims": "(960, 2, 4, 2)"}], "failed": false, "node_type": "Operation"}, "scalar_347": {"edges": [{"target": "transpose_21", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "scalar_348": {"edges": [{"target": "transpose_21", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "reshape_33": {"edges": [{"target": "Conv2d_33", "dims": "(1, 240, 8, 8)"}], "failed": false, "node_type": "Operation"}, "scalar_349": {"edges": [{"target": "reshape_33", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "scalar_350": {"edges": [{"target": "reshape_33", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "scalar_351": {"edges": [{"target": "reshape_33", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "scalar_352": {"edges": [{"target": "reshape_33", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "Conv2d_33": {"edges": [{"target": "batch_norm_30", "dims": "(1, 160, 8, 8)"}], "failed": false, "node_type": "Module"}, "batch_norm_30": {"edges": [{"target": "Identity_137", "dims": "(1, 160, 8, 8)"}], "failed": false, "node_type": "Operation"}, "tensor_146": {"edges": [{"target": "batch_norm_30", "dims": "(160)"}], "failed": false, "node_type": "Constant"}, "tensor_147": {"edges": [{"target": "batch_norm_30", "dims": "(160)"}], "failed": false, "node_type": "Constant"}, "tensor_148": {"edges": [{"target": "batch_norm_30", "dims": "(160)"}], "failed": false, "node_type": "Constant"}, "tensor_149": {"edges": [{"target": "batch_norm_30", "dims": "(160)"}], "failed": false, "node_type": "Constant"}, "scalar_354": {"edges": [{"target": "batch_norm_30", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "scalar_355": {"edges": [{"target": "batch_norm_30", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "scalar_356": {"edges": [{"target": "batch_norm_30", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "Identity_137": {"edges": [{"target": "SiLU_32", "dims": "(1, 160, 8, 8)"}], "failed": false, "node_type": "Module"}, "SiLU_32": {"edges": [{"target": "cat_3", "dims": "(1, 160, 8, 8)"}], "failed": false, "node_type": "Module"}, "cat_3": {"edges": [{"target": "Conv2d_34", "dims": "(1, 320, 8, 8)"}], "failed": false, "node_type": "Operation"}, "Conv2d_34": {"edges": [{"target": "batch_norm_31", "dims": "(1, 160, 8, 8)"}], "failed": false, "node_type": "Module"}, "batch_norm_31": {"edges": [{"target": "Identity_138", "dims": "(1, 160, 8, 8)"}], "failed": false, "node_type": "Operation"}, "tensor_151": {"edges": [{"target": "batch_norm_31", "dims": "(160)"}], "failed": false, "node_type": "Constant"}, "tensor_152": {"edges": [{"target": "batch_norm_31", "dims": "(160)"}], "failed": false, "node_type": "Constant"}, "tensor_153": {"edges": [{"target": "batch_norm_31", "dims": "(160)"}], "failed": false, "node_type": "Constant"}, "tensor_154": {"edges": [{"target": "batch_norm_31", "dims": "(160)"}], "failed": false, "node_type": "Constant"}, "scalar_358": {"edges": [{"target": "batch_norm_31", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "scalar_359": {"edges": [{"target": "batch_norm_31", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "scalar_360": {"edges": [{"target": "batch_norm_31", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "Identity_138": {"edges": [{"target": "SiLU_33", "dims": "(1, 160, 8, 8)"}], "failed": false, "node_type": "Module"}, "SiLU_33": {"edges": [{"target": "Conv2d_35", "dims": "(1, 160, 8, 8)"}], "failed": false, "node_type": "Module"}, "Conv2d_35": {"edges": [{"target": "batch_norm_32", "dims": "(1, 640, 8, 8)"}], "failed": false, "node_type": "Module"}, "batch_norm_32": {"edges": [{"target": "Identity_139", "dims": "(1, 640, 8, 8)"}], "failed": false, "node_type": "Operation"}, "tensor_156": {"edges": [{"target": "batch_norm_32", "dims": "(640)"}], "failed": false, "node_type": "Constant"}, "tensor_157": {"edges": [{"target": "batch_norm_32", "dims": "(640)"}], "failed": false, "node_type": "Constant"}, "tensor_158": {"edges": [{"target": "batch_norm_32", "dims": "(640)"}], "failed": false, "node_type": "Constant"}, "tensor_159": {"edges": [{"target": "batch_norm_32", "dims": "(640)"}], "failed": false, "node_type": "Constant"}, "scalar_362": {"edges": [{"target": "batch_norm_32", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "scalar_363": {"edges": [{"target": "batch_norm_32", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "scalar_364": {"edges": [{"target": "batch_norm_32", "dims": "( )"}], "failed": false, "node_type": "Constant"}, "Identity_139": {"edges": [{"target": "SiLU_34", "dims": "(1, 640, 8, 8)"}], "failed": false, "node_type": "Module"}, "SiLU_34": {"edges": [{"target": "AdaptiveAvgPool2d_1", "dims": "(1, 640, 8, 8)"}], "failed": false, "node_type": "Module"}, "AdaptiveAvgPool2d_1": {"edges": [{"target": "Flatten_1", "dims": "(1, 640, 1, 1)"}], "failed": false, "node_type": "Module"}, "Flatten_1": {"edges": [{"target": "Dropout_28", "dims": "(1, 640)"}], "failed": false, "node_type": "Module"}, "Dropout_28": {"edges": [{"target": "Linear_37", "dims": "(1, 640)"}], "failed": false, "node_type": "Module"}, "Linear_37": {"edges": [{"target": "Identity_140", "dims": "(1, 1000)"}], "failed": false, "node_type": "Module"}, "Identity_140": {"edges": [{"target": "output_0", "dims": "(1, 1000)"}], "failed": false, "node_type": "Module"}, "output_0": {"edges": [], "failed": false, "node_type": "Output"}};
    const module_info = {"ConvNormAct_1": {"type": "ConvNormAct", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "in_channels": 3, "out_channels": 16, "training": true}, "extra_repr": ""}, "Conv2d_1": {"type": "Conv2d", "parameters": {"weight": {"shape": [16, 3, 3, 3], "requires_grad": true}}, "attributes": {"call_super_init": false, "dilation": [1, 1], "dump_patches": false, "groups": 1, "in_channels": 3, "kernel_size": [3, 3], "out_channels": 16, "output_padding": [0, 0], "padding": [1, 1], "padding_mode": "zeros", "stride": [2, 2], "training": true, "transposed": false}, "extra_repr": "3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False"}, "BatchNormAct2d_1": {"type": "BatchNormAct2d", "parameters": {"weight": {"shape": [16], "requires_grad": true}, "bias": {"shape": [16], "requires_grad": true}}, "attributes": {"affine": true, "call_super_init": false, "dump_patches": false, "eps": 1e-05, "momentum": 0.1, "num_features": 16, "track_running_stats": true, "training": true}, "extra_repr": "16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True"}, "Identity_1": {"type": "Identity", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "training": true}, "extra_repr": ""}, "SiLU_1": {"type": "SiLU", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "inplace": true, "training": true}, "extra_repr": "inplace=True"}, "Sequential_1": {"type": "Sequential", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "training": true}, "extra_repr": ""}, "Sequential_2": {"type": "Sequential", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "training": true}, "extra_repr": ""}, "BottleneckBlock_1": {"type": "BottleneckBlock", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "training": true}, "extra_repr": ""}, "ConvNormAct_2": {"type": "ConvNormAct", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "in_channels": 16, "out_channels": 64, "training": true}, "extra_repr": ""}, "Conv2d_2": {"type": "Conv2d", "parameters": {"weight": {"shape": [64, 16, 1, 1], "requires_grad": true}}, "attributes": {"call_super_init": false, "dilation": [1, 1], "dump_patches": false, "groups": 1, "in_channels": 16, "kernel_size": [1, 1], "out_channels": 64, "output_padding": [0, 0], "padding": [0, 0], "padding_mode": "zeros", "stride": [1, 1], "training": true, "transposed": false}, "extra_repr": "16, 64, kernel_size=(1, 1), stride=(1, 1), bias=False"}, "BatchNormAct2d_2": {"type": "BatchNormAct2d", "parameters": {"weight": {"shape": [64], "requires_grad": true}, "bias": {"shape": [64], "requires_grad": true}}, "attributes": {"affine": true, "call_super_init": false, "dump_patches": false, "eps": 1e-05, "momentum": 0.1, "num_features": 64, "track_running_stats": true, "training": true}, "extra_repr": "64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True"}, "Identity_2": {"type": "Identity", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "training": true}, "extra_repr": ""}, "SiLU_2": {"type": "SiLU", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "inplace": true, "training": true}, "extra_repr": "inplace=True"}, "ConvNormAct_3": {"type": "ConvNormAct", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "in_channels": 64, "out_channels": 64, "training": true}, "extra_repr": ""}, "Conv2d_3": {"type": "Conv2d", "parameters": {"weight": {"shape": [64, 1, 3, 3], "requires_grad": true}}, "attributes": {"call_super_init": false, "dilation": [1, 1], "dump_patches": false, "groups": 64, "in_channels": 64, "kernel_size": [3, 3], "out_channels": 64, "output_padding": [0, 0], "padding": [1, 1], "padding_mode": "zeros", "stride": [1, 1], "training": true, "transposed": false}, "extra_repr": "64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=64, bias=False"}, "BatchNormAct2d_3": {"type": "BatchNormAct2d", "parameters": {"weight": {"shape": [64], "requires_grad": true}, "bias": {"shape": [64], "requires_grad": true}}, "attributes": {"affine": true, "call_super_init": false, "dump_patches": false, "eps": 1e-05, "momentum": 0.1, "num_features": 64, "track_running_stats": true, "training": true}, "extra_repr": "64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True"}, "Identity_3": {"type": "Identity", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "training": true}, "extra_repr": ""}, "SiLU_3": {"type": "SiLU", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "inplace": true, "training": true}, "extra_repr": "inplace=True"}, "Identity_4": {"type": "Identity", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "training": true}, "extra_repr": ""}, 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"attributes": {"affine": true, "call_super_init": false, "dump_patches": false, "eps": 1e-05, "momentum": 0.1, "num_features": 160, "track_running_stats": true, "training": true}, "extra_repr": "160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True"}, "Identity_137": {"type": "Identity", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "training": true}, "extra_repr": ""}, "SiLU_32": {"type": "SiLU", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "inplace": true, "training": true}, "extra_repr": "inplace=True"}, "ConvNormAct_31": {"type": "ConvNormAct", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "in_channels": 320, "out_channels": 160, "training": true}, "extra_repr": ""}, "Conv2d_34": {"type": "Conv2d", "parameters": {"weight": {"shape": [160, 320, 3, 3], "requires_grad": true}}, "attributes": {"call_super_init": false, "dilation": [1, 1], "dump_patches": false, "groups": 1, "in_channels": 320, "kernel_size": [3, 3], "out_channels": 160, "output_padding": [0, 0], "padding": [1, 1], "padding_mode": "zeros", "stride": [1, 1], "training": true, "transposed": false}, "extra_repr": "320, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False"}, "BatchNormAct2d_31": {"type": "BatchNormAct2d", "parameters": {"weight": {"shape": [160], "requires_grad": true}, "bias": {"shape": [160], "requires_grad": true}}, "attributes": {"affine": true, "call_super_init": false, "dump_patches": false, "eps": 1e-05, "momentum": 0.1, "num_features": 160, "track_running_stats": true, "training": true}, "extra_repr": "160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True"}, "Identity_138": {"type": "Identity", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "training": true}, "extra_repr": ""}, "SiLU_33": {"type": "SiLU", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "inplace": true, "training": true}, "extra_repr": "inplace=True"}, "ConvNormAct_32": {"type": "ConvNormAct", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "in_channels": 160, "out_channels": 640, "training": true}, "extra_repr": ""}, "Conv2d_35": {"type": "Conv2d", "parameters": {"weight": {"shape": [640, 160, 1, 1], "requires_grad": true}}, "attributes": {"call_super_init": false, "dilation": [1, 1], "dump_patches": false, "groups": 1, "in_channels": 160, "kernel_size": [1, 1], "out_channels": 640, "output_padding": [0, 0], "padding": [0, 0], "padding_mode": "zeros", "stride": [1, 1], "training": true, "transposed": false}, "extra_repr": "160, 640, kernel_size=(1, 1), stride=(1, 1), bias=False"}, "BatchNormAct2d_32": {"type": "BatchNormAct2d", "parameters": {"weight": {"shape": [640], "requires_grad": true}, "bias": {"shape": [640], "requires_grad": true}}, "attributes": {"affine": true, "call_super_init": false, "dump_patches": false, "eps": 1e-05, "momentum": 0.1, "num_features": 640, "track_running_stats": true, "training": true}, "extra_repr": "640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True"}, "Identity_139": {"type": "Identity", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "training": true}, "extra_repr": ""}, "SiLU_34": {"type": "SiLU", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "inplace": true, "training": true}, "extra_repr": "inplace=True"}, "ClassifierHead_1": {"type": "ClassifierHead", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "in_features": 640, "input_fmt": "NCHW", "training": true, "use_conv": false}, "extra_repr": ""}, "SelectAdaptivePool2d_1": {"type": "SelectAdaptivePool2d", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "pool_type": "avg", "training": true}, "extra_repr": ""}, "AdaptiveAvgPool2d_1": {"type": "AdaptiveAvgPool2d", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "output_size": 1, "training": true}, "extra_repr": "output_size=1"}, "Flatten_1": {"type": "Flatten", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "end_dim": -1, "start_dim": 1, "training": true}, "extra_repr": "start_dim=1, end_dim=-1"}, "Dropout_28": {"type": "Dropout", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "inplace": false, "p": 0.0, "training": true}, "extra_repr": "p=0.0, inplace=False"}, "Linear_37": {"type": "Linear", "parameters": {"weight": {"shape": [1000, 640], "requires_grad": true}, "bias": {"shape": [1000], "requires_grad": true}}, "attributes": {"call_super_init": false, "dump_patches": false, "in_features": 640, "out_features": 1000, "training": true}, "extra_repr": "in_features=640, out_features=1000, bias=True"}, "Identity_140": {"type": "Identity", "parameters": {}, "attributes": {"call_super_init": false, "dump_patches": false, "training": true}, "extra_repr": ""}};
    const func_info_map = {"Conv2d_1": {"positional_args": ["tensor(1,3,256,256)"], "keyword_args": {}}, "add__1": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_1": {"positional_args": ["tensor(1,16,128,128)", "tensor(16)", "tensor(16)", "tensor(16)", "tensor(16)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_1": {"positional_args": ["tensor(1,16,128,128)"], "keyword_args": {}}, "SiLU_1": {"positional_args": ["tensor(1,16,128,128)"], "keyword_args": {}}, "Conv2d_2": {"positional_args": ["tensor(1,16,128,128)"], "keyword_args": {}}, "add__2": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_2": {"positional_args": ["tensor(1,64,128,128)", "tensor(64)", "tensor(64)", "tensor(64)", "tensor(64)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_2": {"positional_args": ["tensor(1,64,128,128)"], "keyword_args": {}}, "SiLU_2": {"positional_args": ["tensor(1,64,128,128)"], "keyword_args": {}}, "Conv2d_3": {"positional_args": ["tensor(1,64,128,128)"], "keyword_args": {}}, "add__3": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_3": {"positional_args": ["tensor(1,64,128,128)", "tensor(64)", "tensor(64)", "tensor(64)", "tensor(64)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_3": {"positional_args": ["tensor(1,64,128,128)"], "keyword_args": {}}, "SiLU_3": {"positional_args": ["tensor(1,64,128,128)"], "keyword_args": {}}, "Identity_4": {"positional_args": ["tensor(1,64,128,128)"], "keyword_args": {}}, "Identity_5": {"positional_args": ["tensor(1,64,128,128)"], "keyword_args": {}}, "Conv2d_4": {"positional_args": ["tensor(1,64,128,128)"], "keyword_args": {}}, "add__4": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_4": {"positional_args": ["tensor(1,32,128,128)", "tensor(32)", "tensor(32)", "tensor(32)", "tensor(32)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_6": {"positional_args": ["tensor(1,32,128,128)"], "keyword_args": {}}, "Identity_7": {"positional_args": ["tensor(1,32,128,128)"], "keyword_args": {}}, "Identity_8": {"positional_args": ["tensor(1,32,128,128)"], "keyword_args": {}}, "Identity_9": {"positional_args": ["tensor(1,32,128,128)"], "keyword_args": {}}, "Identity_10": {"positional_args": ["tensor(1,32,128,128)"], "keyword_args": {}}, "Conv2d_5": {"positional_args": ["tensor(1,32,128,128)"], "keyword_args": {}}, "add__5": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_5": {"positional_args": ["tensor(1,128,128,128)", "tensor(128)", "tensor(128)", "tensor(128)", "tensor(128)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_11": {"positional_args": ["tensor(1,128,128,128)"], "keyword_args": {}}, "SiLU_4": {"positional_args": ["tensor(1,128,128,128)"], "keyword_args": {}}, "Conv2d_6": {"positional_args": ["tensor(1,128,128,128)"], "keyword_args": {}}, "add__6": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_6": {"positional_args": ["tensor(1,128,64,64)", "tensor(128)", "tensor(128)", "tensor(128)", "tensor(128)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_12": {"positional_args": ["tensor(1,128,64,64)"], "keyword_args": {}}, "SiLU_5": {"positional_args": ["tensor(1,128,64,64)"], "keyword_args": {}}, "Identity_13": {"positional_args": ["tensor(1,128,64,64)"], "keyword_args": {}}, "Identity_14": {"positional_args": ["tensor(1,128,64,64)"], "keyword_args": {}}, "Conv2d_7": {"positional_args": ["tensor(1,128,64,64)"], "keyword_args": {}}, "add__7": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_7": {"positional_args": ["tensor(1,64,64,64)", "tensor(64)", "tensor(64)", "tensor(64)", "tensor(64)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_15": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "Identity_16": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "Identity_17": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "Identity_18": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "Identity_19": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "Conv2d_8": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "add__8": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_8": {"positional_args": ["tensor(1,256,64,64)", "tensor(256)", "tensor(256)", "tensor(256)", "tensor(256)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_20": {"positional_args": ["tensor(1,256,64,64)"], "keyword_args": {}}, "SiLU_6": {"positional_args": ["tensor(1,256,64,64)"], "keyword_args": {}}, "Conv2d_9": {"positional_args": ["tensor(1,256,64,64)"], "keyword_args": {}}, "add__9": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_9": {"positional_args": ["tensor(1,256,64,64)", "tensor(256)", "tensor(256)", "tensor(256)", "tensor(256)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_21": {"positional_args": ["tensor(1,256,64,64)"], "keyword_args": {}}, "SiLU_7": {"positional_args": ["tensor(1,256,64,64)"], "keyword_args": {}}, "Identity_22": {"positional_args": ["tensor(1,256,64,64)"], "keyword_args": {}}, "Identity_23": {"positional_args": ["tensor(1,256,64,64)"], "keyword_args": {}}, "Conv2d_10": {"positional_args": ["tensor(1,256,64,64)"], "keyword_args": {}}, "add__10": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_10": {"positional_args": ["tensor(1,64,64,64)", "tensor(64)", "tensor(64)", "tensor(64)", "tensor(64)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_24": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "Identity_25": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "Identity_26": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "Identity_27": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "Identity_28": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "__add___1": {"positional_args": ["tensor(1,64,64,64)", "tensor(1,64,64,64)"], "keyword_args": {}}, "Identity_29": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "Conv2d_11": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "add__11": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_11": {"positional_args": ["tensor(1,256,64,64)", "tensor(256)", "tensor(256)", "tensor(256)", "tensor(256)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_30": {"positional_args": ["tensor(1,256,64,64)"], "keyword_args": {}}, "SiLU_8": {"positional_args": ["tensor(1,256,64,64)"], "keyword_args": {}}, "Conv2d_12": {"positional_args": ["tensor(1,256,64,64)"], "keyword_args": {}}, "add__12": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_12": {"positional_args": ["tensor(1,256,64,64)", "tensor(256)", "tensor(256)", "tensor(256)", "tensor(256)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_31": {"positional_args": ["tensor(1,256,64,64)"], "keyword_args": {}}, "SiLU_9": {"positional_args": ["tensor(1,256,64,64)"], "keyword_args": {}}, "Identity_32": {"positional_args": ["tensor(1,256,64,64)"], "keyword_args": {}}, "Identity_33": {"positional_args": ["tensor(1,256,64,64)"], "keyword_args": {}}, "Conv2d_13": {"positional_args": ["tensor(1,256,64,64)"], "keyword_args": {}}, "add__13": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_13": {"positional_args": ["tensor(1,64,64,64)", "tensor(64)", "tensor(64)", "tensor(64)", "tensor(64)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_34": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "Identity_35": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "Identity_36": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "Identity_37": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "Identity_38": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "__add___2": {"positional_args": ["tensor(1,64,64,64)", "tensor(1,64,64,64)"], "keyword_args": {}}, "Identity_39": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "Conv2d_14": {"positional_args": ["tensor(1,64,64,64)"], "keyword_args": {}}, "add__14": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_14": {"positional_args": ["tensor(1,256,64,64)", "tensor(256)", "tensor(256)", "tensor(256)", "tensor(256)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_40": {"positional_args": ["tensor(1,256,64,64)"], "keyword_args": {}}, "SiLU_10": {"positional_args": ["tensor(1,256,64,64)"], "keyword_args": {}}, "Conv2d_15": {"positional_args": ["tensor(1,256,64,64)"], "keyword_args": {}}, "add__15": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_15": {"positional_args": ["tensor(1,256,32,32)", "tensor(256)", "tensor(256)", "tensor(256)", "tensor(256)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_41": {"positional_args": ["tensor(1,256,32,32)"], "keyword_args": {}}, "SiLU_11": {"positional_args": ["tensor(1,256,32,32)"], "keyword_args": {}}, "Identity_42": {"positional_args": ["tensor(1,256,32,32)"], "keyword_args": {}}, "Identity_43": {"positional_args": ["tensor(1,256,32,32)"], "keyword_args": {}}, "Conv2d_16": {"positional_args": ["tensor(1,256,32,32)"], "keyword_args": {}}, "add__16": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_16": {"positional_args": ["tensor(1,96,32,32)", "tensor(96)", "tensor(96)", "tensor(96)", "tensor(96)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_44": {"positional_args": ["tensor(1,96,32,32)"], "keyword_args": {}}, "Identity_45": {"positional_args": ["tensor(1,96,32,32)"], "keyword_args": {}}, "Identity_46": {"positional_args": ["tensor(1,96,32,32)"], "keyword_args": {}}, "Identity_47": {"positional_args": ["tensor(1,96,32,32)"], "keyword_args": {}}, "Identity_48": {"positional_args": ["tensor(1,96,32,32)"], "keyword_args": {}}, "Conv2d_17": {"positional_args": ["tensor(1,96,32,32)"], "keyword_args": {}}, "add__17": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_17": {"positional_args": ["tensor(1,96,32,32)", "tensor(96)", "tensor(96)", "tensor(96)", "tensor(96)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_49": {"positional_args": ["tensor(1,96,32,32)"], "keyword_args": {}}, "SiLU_12": {"positional_args": ["tensor(1,96,32,32)"], "keyword_args": {}}, "Conv2d_18": {"positional_args": ["tensor(1,96,32,32)"], "keyword_args": {}}, "reshape_1": {"positional_args": ["tensor(1,144,32,32)", 2304, 2, 16, 2], "keyword_args": {}}, "transpose_1": {"positional_args": ["tensor(2304,2,16,2)", 1, 2], "keyword_args": {}}, "reshape_2": {"positional_args": ["tensor(2304,16,2,2)", 1, 144, 256, 4], "keyword_args": {}}, "transpose_2": {"positional_args": ["tensor(1,144,256,4)", 1, 3], "keyword_args": {}}, "reshape_3": {"positional_args": ["tensor(1,4,256,144)", 4, 256, -1], "keyword_args": {}}, "LayerNorm_1": {"positional_args": ["tensor(4,256,144)"], "keyword_args": {}}, "Linear_1": {"positional_args": ["tensor(4,256,144)"], "keyword_args": {}}, "reshape_4": {"positional_args": ["tensor(4,256,432)", 4, 256, 3, 4, 36], "keyword_args": {}}, "permute_1": {"positional_args": ["tensor(4,256,3,4,36)", 2, 0, 3, 1, 4], "keyword_args": {}}, "unbind_1": {"positional_args": ["tensor(3,4,4,256,36)", 0], "keyword_args": {}}, "Identity_50": {"positional_args": ["tensor(4,4,256,36)"], "keyword_args": {}}, "Identity_51": {"positional_args": ["tensor(4,4,256,36)"], "keyword_args": {}}, "scaled_dot_product_attention_1": {"positional_args": ["tensor(4,4,256,36)", "tensor(4,4,256,36)", "tensor(4,4,256,36)"], "keyword_args": {"dropout_p": 0.0}}, "transpose_3": {"positional_args": ["tensor(4,4,256,36)", 1, 2], "keyword_args": {}}, "reshape_5": {"positional_args": ["tensor(4,256,4,36)", 4, 256, 144], "keyword_args": {}}, "Linear_2": {"positional_args": ["tensor(4,256,144)"], "keyword_args": {}}, "Dropout_1": {"positional_args": ["tensor(4,256,144)"], "keyword_args": {}}, "Identity_52": {"positional_args": ["tensor(4,256,144)"], "keyword_args": {}}, "Identity_53": {"positional_args": ["tensor(4,256,144)"], "keyword_args": {}}, "__add___3": {"positional_args": ["tensor(4,256,144)", "tensor(4,256,144)"], "keyword_args": {}}, "LayerNorm_2": {"positional_args": ["tensor(4,256,144)"], "keyword_args": {}}, "Linear_3": {"positional_args": ["tensor(4,256,144)"], "keyword_args": {}}, "SiLU_13": {"positional_args": ["tensor(4,256,288)"], "keyword_args": {}}, "Dropout_2": {"positional_args": ["tensor(4,256,288)"], "keyword_args": {}}, "Identity_54": {"positional_args": ["tensor(4,256,288)"], "keyword_args": {}}, "Linear_4": {"positional_args": ["tensor(4,256,288)"], "keyword_args": {}}, "Dropout_3": {"positional_args": ["tensor(4,256,144)"], "keyword_args": {}}, "Identity_55": {"positional_args": ["tensor(4,256,144)"], "keyword_args": {}}, "Identity_56": {"positional_args": ["tensor(4,256,144)"], "keyword_args": {}}, "__add___4": 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"tensor(4,16,240)"], "keyword_args": {}}, "LayerNorm_16": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "Linear_27": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "SiLU_29": {"positional_args": ["tensor(4,16,480)"], "keyword_args": {}}, "Dropout_20": {"positional_args": ["tensor(4,16,480)"], "keyword_args": {}}, "Identity_120": {"positional_args": ["tensor(4,16,480)"], "keyword_args": {}}, "Linear_28": {"positional_args": ["tensor(4,16,480)"], "keyword_args": {}}, "Dropout_21": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "Identity_121": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "Identity_122": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "__add___16": {"positional_args": ["tensor(4,16,240)", "tensor(4,16,240)"], "keyword_args": {}}, "LayerNorm_17": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "Linear_29": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "reshape_28": {"positional_args": ["tensor(4,16,720)", 4, 16, 3, 4, 60], "keyword_args": {}}, "permute_8": {"positional_args": ["tensor(4,16,3,4,60)", 2, 0, 3, 1, 4], "keyword_args": {}}, "unbind_8": {"positional_args": ["tensor(3,4,4,16,60)", 0], "keyword_args": {}}, "Identity_123": {"positional_args": ["tensor(4,4,16,60)"], "keyword_args": {}}, "Identity_124": {"positional_args": ["tensor(4,4,16,60)"], "keyword_args": {}}, "scaled_dot_product_attention_8": {"positional_args": ["tensor(4,4,16,60)", "tensor(4,4,16,60)", "tensor(4,4,16,60)"], "keyword_args": {"dropout_p": 0.0}}, "transpose_18": {"positional_args": ["tensor(4,4,16,60)", 1, 2], "keyword_args": {}}, "reshape_29": {"positional_args": ["tensor(4,16,4,60)", 4, 16, 240], "keyword_args": {}}, "Linear_30": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "Dropout_22": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "Identity_125": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "Identity_126": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "__add___17": {"positional_args": ["tensor(4,16,240)", "tensor(4,16,240)"], "keyword_args": {}}, "LayerNorm_18": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "Linear_31": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "SiLU_30": {"positional_args": ["tensor(4,16,480)"], "keyword_args": {}}, "Dropout_23": {"positional_args": ["tensor(4,16,480)"], "keyword_args": {}}, "Identity_127": {"positional_args": ["tensor(4,16,480)"], "keyword_args": {}}, "Linear_32": {"positional_args": ["tensor(4,16,480)"], "keyword_args": {}}, "Dropout_24": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "Identity_128": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "Identity_129": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "__add___18": {"positional_args": ["tensor(4,16,240)", "tensor(4,16,240)"], "keyword_args": {}}, "LayerNorm_19": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "Linear_33": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "reshape_30": {"positional_args": ["tensor(4,16,720)", 4, 16, 3, 4, 60], "keyword_args": {}}, "permute_9": {"positional_args": ["tensor(4,16,3,4,60)", 2, 0, 3, 1, 4], "keyword_args": {}}, "unbind_9": {"positional_args": ["tensor(3,4,4,16,60)", 0], "keyword_args": {}}, "Identity_130": {"positional_args": ["tensor(4,4,16,60)"], "keyword_args": {}}, "Identity_131": {"positional_args": ["tensor(4,4,16,60)"], "keyword_args": {}}, "scaled_dot_product_attention_9": {"positional_args": ["tensor(4,4,16,60)", "tensor(4,4,16,60)", "tensor(4,4,16,60)"], "keyword_args": {"dropout_p": 0.0}}, "transpose_19": {"positional_args": ["tensor(4,4,16,60)", 1, 2], "keyword_args": {}}, "reshape_31": {"positional_args": ["tensor(4,16,4,60)", 4, 16, 240], "keyword_args": {}}, "Linear_34": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "Dropout_25": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "Identity_132": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "Identity_133": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "__add___19": {"positional_args": ["tensor(4,16,240)", "tensor(4,16,240)"], "keyword_args": {}}, "LayerNorm_20": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "Linear_35": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "SiLU_31": {"positional_args": ["tensor(4,16,480)"], "keyword_args": {}}, "Dropout_26": {"positional_args": ["tensor(4,16,480)"], "keyword_args": {}}, "Identity_134": {"positional_args": ["tensor(4,16,480)"], "keyword_args": {}}, "Linear_36": {"positional_args": ["tensor(4,16,480)"], "keyword_args": {}}, "Dropout_27": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "Identity_135": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "Identity_136": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "__add___20": {"positional_args": ["tensor(4,16,240)", "tensor(4,16,240)"], "keyword_args": {}}, "LayerNorm_21": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "contiguous_3": {"positional_args": ["tensor(4,16,240)"], "keyword_args": {}}, "view_3": {"positional_args": ["tensor(4,16,240)", 1, 4, 16, -1], "keyword_args": {}}, "transpose_20": {"positional_args": ["tensor(1,4,16,240)", 1, 3], "keyword_args": {}}, "reshape_32": {"positional_args": ["tensor(1,240,16,4)", 960, 4, 2, 2], "keyword_args": {}}, "transpose_21": {"positional_args": ["tensor(960,4,2,2)", 1, 2], "keyword_args": {}}, "reshape_33": {"positional_args": ["tensor(960,2,4,2)", 1, 240, 8, 8], "keyword_args": {}}, "Conv2d_33": {"positional_args": ["tensor(1,240,8,8)"], "keyword_args": {}}, "add__30": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_30": {"positional_args": ["tensor(1,160,8,8)", "tensor(160)", "tensor(160)", "tensor(160)", "tensor(160)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_137": {"positional_args": ["tensor(1,160,8,8)"], "keyword_args": {}}, "SiLU_32": {"positional_args": ["tensor(1,160,8,8)"], "keyword_args": {}}, "cat_3": {"positional_args": ["['tensor(1,160,8,8)', 'tensor(1,160,8,8)']"], "keyword_args": {"dim": 1}}, "Conv2d_34": {"positional_args": ["tensor(1,320,8,8)"], "keyword_args": {}}, "add__31": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_31": {"positional_args": ["tensor(1,160,8,8)", "tensor(160)", "tensor(160)", "tensor(160)", "tensor(160)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_138": {"positional_args": ["tensor(1,160,8,8)"], "keyword_args": {}}, "SiLU_33": {"positional_args": ["tensor(1,160,8,8)"], "keyword_args": {}}, "Conv2d_35": {"positional_args": ["tensor(1,160,8,8)"], "keyword_args": {}}, "add__32": {"positional_args": ["tensor()", 1], "keyword_args": {}}, "batch_norm_32": {"positional_args": ["tensor(1,640,8,8)", "tensor(640)", "tensor(640)", "tensor(640)", "tensor(640)", true, 0.1, 1e-05], "keyword_args": {}}, "Identity_139": {"positional_args": ["tensor(1,640,8,8)"], "keyword_args": {}}, "SiLU_34": {"positional_args": ["tensor(1,640,8,8)"], "keyword_args": {}}, "AdaptiveAvgPool2d_1": {"positional_args": ["tensor(1,640,8,8)"], "keyword_args": {}}, "Flatten_1": {"positional_args": ["tensor(1,640,1,1)"], "keyword_args": {}}, "Dropout_28": {"positional_args": ["tensor(1,640)"], "keyword_args": {}}, "Linear_37": {"positional_args": ["tensor(1,640)"], "keyword_args": {}}, "Identity_140": {"positional_args": ["tensor(1,1000)"], "keyword_args": {}}};
    const module_name_to_base_name = {"input_0": "input_0", "ConvNormAct_1": "ConvNormAct_1", "Conv2d_1": "Conv2d_1", "BatchNormAct2d_1": "BatchNormAct2d_1", "add__1": "add__1", "batch_norm_1": "batch_norm_1", "Identity_1": "Identity_1", "SiLU_1": "SiLU_1", "Sequential_1": "Sequential_1", "Sequential_2": "Sequential_2", "BottleneckBlock_1": "BottleneckBlock_1", "ConvNormAct_2": "ConvNormAct_2", "Conv2d_2": "Conv2d_2", "BatchNormAct2d_2": "BatchNormAct2d_2", "add__2": "add__2", "batch_norm_2": "batch_norm_2", "Identity_2": "Identity_2", "SiLU_2": "SiLU_2", "ConvNormAct_3": "ConvNormAct_3", "Conv2d_3": "Conv2d_3", "BatchNormAct2d_3": "BatchNormAct2d_3", "add__3": "add__3", "batch_norm_3": "batch_norm_3", "Identity_3": "Identity_3", "SiLU_3": "SiLU_3", "Identity_4": "Identity_4", "Identity_5": "Identity_5", "ConvNormAct_4": "ConvNormAct_4", "Conv2d_4": "Conv2d_4", "BatchNormAct2d_4": "BatchNormAct2d_4", "add__4": "add__4", "batch_norm_4": "batch_norm_4", "Identity_6": "Identity_6", "Identity_7": "Identity_7", "Identity_8": "Identity_8", "Identity_9": "Identity_9", "Identity_10": "Identity_10", "Sequential_3": "Sequential_3", "BottleneckBlock_2": "BottleneckBlock_2", "ConvNormAct_5": "ConvNormAct_5", "Conv2d_5": "Conv2d_5", "BatchNormAct2d_5": "BatchNormAct2d_5", "add__5": "add__5", "batch_norm_5": "batch_norm_5", "Identity_11": "Identity_11", "SiLU_4": "SiLU_4", "ConvNormAct_6": "ConvNormAct_6", "Conv2d_6": "Conv2d_6", "BatchNormAct2d_6": "BatchNormAct2d_6", "add__6": "add__6", "batch_norm_6": "batch_norm_6", "Identity_12": "Identity_12", "SiLU_5": "SiLU_5", "Identity_13": "Identity_13", "Identity_14": "Identity_14", "ConvNormAct_7": "ConvNormAct_7", "Conv2d_7": "Conv2d_7", "BatchNormAct2d_7": "BatchNormAct2d_7", "add__7": "add__7", "batch_norm_7": "batch_norm_7", "Identity_15": "Identity_15", "Identity_16": "Identity_16", "Identity_17": "Identity_17", "Identity_18": "Identity_18", "Identity_19": "Identity_19", "BottleneckBlock_3": "BottleneckBlock_3", "ConvNormAct_8": "ConvNormAct_8", "Conv2d_8": "Conv2d_8", "BatchNormAct2d_8": "BatchNormAct2d_8", "add__8": "add__8", "batch_norm_8": "batch_norm_8", "Identity_20": "Identity_20", "SiLU_6": "SiLU_6", "ConvNormAct_9": "ConvNormAct_9", "Conv2d_9": "Conv2d_9", "BatchNormAct2d_9": "BatchNormAct2d_9", "add__9": "add__9", "batch_norm_9": "batch_norm_9", "Identity_21": "Identity_21", "SiLU_7": "SiLU_7", "Identity_22": "Identity_22", "Identity_23": "Identity_23", "ConvNormAct_10": "ConvNormAct_10", "Conv2d_10": "Conv2d_10", "BatchNormAct2d_10": "BatchNormAct2d_10", "add__10": "add__10", "batch_norm_10": "batch_norm_10", "Identity_24": "Identity_24", "Identity_25": "Identity_25", "Identity_26": "Identity_26", "Identity_27": "Identity_27", "Identity_28": "Identity_28", "__add___1": "__add___1", "Identity_29": "Identity_29", "BottleneckBlock_4": "BottleneckBlock_4", "ConvNormAct_11": "ConvNormAct_11", "Conv2d_11": "Conv2d_11", "BatchNormAct2d_11": "BatchNormAct2d_11", "add__11": "add__11", "batch_norm_11": "batch_norm_11", "Identity_30": "Identity_30", "SiLU_8": "SiLU_8", "ConvNormAct_12": "ConvNormAct_12", "Conv2d_12": "Conv2d_12", "BatchNormAct2d_12": "BatchNormAct2d_12", "add__12": "add__12", "batch_norm_12": "batch_norm_12", "Identity_31": "Identity_31", "SiLU_9": "SiLU_9", "Identity_32": "Identity_32", "Identity_33": "Identity_33", "ConvNormAct_13": "ConvNormAct_13", "Conv2d_13": "Conv2d_13", "BatchNormAct2d_13": "BatchNormAct2d_13", "add__13": "add__13", "batch_norm_13": "batch_norm_13", "Identity_34": "Identity_34", "Identity_35": "Identity_35", "Identity_36": "Identity_36", "Identity_37": "Identity_37", "Identity_38": "Identity_38", "__add___2": "__add___2", "Identity_39": "Identity_39", "Sequential_4": "Sequential_4", "BottleneckBlock_5": "BottleneckBlock_5", "ConvNormAct_14": "ConvNormAct_14", "Conv2d_14": "Conv2d_14", "BatchNormAct2d_14": "BatchNormAct2d_14", "add__14": "add__14", "batch_norm_14": "batch_norm_14", "Identity_40": "Identity_40", "SiLU_10": "SiLU_10", "ConvNormAct_15": "ConvNormAct_15", "Conv2d_15": "Conv2d_15", "BatchNormAct2d_15": "BatchNormAct2d_15", "add__15": "add__15", "batch_norm_15": "batch_norm_15", "Identity_41": "Identity_41", "SiLU_11": "SiLU_11", "Identity_42": "Identity_42", "Identity_43": "Identity_43", "ConvNormAct_16": "ConvNormAct_16", "Conv2d_16": "Conv2d_16", "BatchNormAct2d_16": "BatchNormAct2d_16", "add__16": "add__16", "batch_norm_16": "batch_norm_16", "Identity_44": "Identity_44", "Identity_45": "Identity_45", "Identity_46": "Identity_46", "Identity_47": "Identity_47", "Identity_48": "Identity_48", "MobileVitBlock_1": "MobileVitBlock_1", "ConvNormAct_17": "ConvNormAct_17", "Conv2d_17": "Conv2d_17", "BatchNormAct2d_17": "BatchNormAct2d_17", "add__17": "add__17", "batch_norm_17": "batch_norm_17", "Identity_49": "Identity_49", "SiLU_12": "SiLU_12", "Conv2d_18": "Conv2d_18", "reshape_1": "reshape_1", "transpose_1": "transpose_1", "reshape_2": "reshape_2", "transpose_2": "transpose_2", "reshape_3": "reshape_3", "Sequential_5": "Sequential_5", "Block_1": "Block_1", "LayerNorm_1": "LayerNorm_1", "Attention_1": "Attention_1", "Linear_1": "Linear_1", "reshape_4": "reshape_4", "permute_1": "permute_1", "unbind_1": "unbind_1", "Identity_50": "Identity_50", "Identity_51": "Identity_51", "scaled_dot_product_attention_1": "scaled_dot_product_attention_1", "transpose_3": "transpose_3", "reshape_5": "reshape_5", "Linear_2": "Linear_2", "Dropout_1": "Dropout_1", "Identity_52": "Identity_52", "Identity_53": "Identity_53", "__add___3": "__add___3", "LayerNorm_2": "LayerNorm_2", "Mlp_1": "Mlp_1", "Linear_3": "Linear_3", "SiLU_13": "SiLU_13", "Dropout_2": "Dropout_2", "Identity_54": "Identity_54", "Linear_4": "Linear_4", "Dropout_3": "Dropout_3", "Identity_55": "Identity_55", "Identity_56": "Identity_56", "__add___4": "__add___4", "Block_2": "Block_2", "LayerNorm_3": "LayerNorm_3", "Attention_2": "Attention_2", "Linear_5": "Linear_5", "reshape_6": "reshape_6", "permute_2": "permute_2", "unbind_2": "unbind_2", "Identity_57": "Identity_57", "Identity_58": "Identity_58", "scaled_dot_product_attention_2": "scaled_dot_product_attention_2", "transpose_4": "transpose_4", "reshape_7": "reshape_7", "Linear_6": "Linear_6", "Dropout_4": "Dropout_4", "Identity_59": "Identity_59", "Identity_60": "Identity_60", "__add___5": "__add___5", "LayerNorm_4": "LayerNorm_4", "Mlp_2": "Mlp_2", "Linear_7": "Linear_7", "SiLU_14": "SiLU_14", "Dropout_5": "Dropout_5", "Identity_61": "Identity_61", "Linear_8": "Linear_8", "Dropout_6": "Dropout_6", "Identity_62": "Identity_62", "Identity_63": "Identity_63", "__add___6": "__add___6", "LayerNorm_5": "LayerNorm_5", "contiguous_1": "contiguous_1", "view_1": "view_1", "transpose_5": "transpose_5", "reshape_8": "reshape_8", "transpose_6": "transpose_6", "reshape_9": "reshape_9", "ConvNormAct_18": "ConvNormAct_18", "Conv2d_19": "Conv2d_19", "BatchNormAct2d_18": "BatchNormAct2d_18", "add__18": "add__18", "batch_norm_18": "batch_norm_18", "Identity_64": "Identity_64", "SiLU_15": "SiLU_15", "cat_1": "cat_1", "ConvNormAct_19": "ConvNormAct_19", "Conv2d_20": "Conv2d_20", "BatchNormAct2d_19": "BatchNormAct2d_19", "add__19": "add__19", "batch_norm_19": "batch_norm_19", "Identity_65": "Identity_65", "SiLU_16": "SiLU_16", "Sequential_6": "Sequential_6", "BottleneckBlock_6": "BottleneckBlock_6", "ConvNormAct_20": "ConvNormAct_20", "Conv2d_21": "Conv2d_21", "BatchNormAct2d_20": "BatchNormAct2d_20", "add__20": "add__20", "batch_norm_20": "batch_norm_20", "Identity_66": "Identity_66", "SiLU_17": "SiLU_17", "ConvNormAct_21": "ConvNormAct_21", "Conv2d_22": "Conv2d_22", "BatchNormAct2d_21": "BatchNormAct2d_21", "add__21": "add__21", "batch_norm_21": "batch_norm_21", "Identity_67": "Identity_67", "SiLU_18": "SiLU_18", "Identity_68": "Identity_68", "Identity_69": "Identity_69", "ConvNormAct_22": "ConvNormAct_22", "Conv2d_23": "Conv2d_23", "BatchNormAct2d_22": "BatchNormAct2d_22", "add__22": "add__22", "batch_norm_22": "batch_norm_22", "Identity_70": "Identity_70", "Identity_71": "Identity_71", "Identity_72": "Identity_72", "Identity_73": "Identity_73", "Identity_74": "Identity_74", "MobileVitBlock_2": "MobileVitBlock_2", "ConvNormAct_23": "ConvNormAct_23", "Conv2d_24": "Conv2d_24", "BatchNormAct2d_23": "BatchNormAct2d_23", "add__23": "add__23", "batch_norm_23": "batch_norm_23", "Identity_75": "Identity_75", "SiLU_19": "SiLU_19", "Conv2d_25": "Conv2d_25", "reshape_10": "reshape_10", "transpose_7": "transpose_7", "reshape_11": "reshape_11", "transpose_8": "transpose_8", "reshape_12": "reshape_12", "Sequential_7": "Sequential_7", "Block_3": "Block_3", "LayerNorm_6": "LayerNorm_6", "Attention_3": "Attention_3", "Linear_9": "Linear_9", "reshape_13": "reshape_13", "permute_3": "permute_3", "unbind_3": "unbind_3", "Identity_76": "Identity_76", "Identity_77": "Identity_77", "scaled_dot_product_attention_3": "scaled_dot_product_attention_3", "transpose_9": "transpose_9", "reshape_14": "reshape_14", "Linear_10": "Linear_10", "Dropout_7": "Dropout_7", "Identity_78": "Identity_78", "Identity_79": "Identity_79", "__add___7": "__add___7", "LayerNorm_7": "LayerNorm_7", "Mlp_3": "Mlp_3", "Linear_11": "Linear_11", "SiLU_20": "SiLU_20", "Dropout_8": "Dropout_8", "Identity_80": "Identity_80", "Linear_12": "Linear_12", "Dropout_9": "Dropout_9", "Identity_81": "Identity_81", "Identity_82": "Identity_82", "__add___8": "__add___8", "Block_4": "Block_4", "LayerNorm_8": "LayerNorm_8", "Attention_4": "Attention_4", "Linear_13": "Linear_13", "reshape_15": "reshape_15", "permute_4": "permute_4", "unbind_4": "unbind_4", "Identity_83": "Identity_83", "Identity_84": "Identity_84", "scaled_dot_product_attention_4": "scaled_dot_product_attention_4", "transpose_10": "transpose_10", "reshape_16": "reshape_16", "Linear_14": "Linear_14", "Dropout_10": "Dropout_10", "Identity_85": "Identity_85", "Identity_86": "Identity_86", "__add___9": "__add___9", "LayerNorm_9": "LayerNorm_9", "Mlp_4": "Mlp_4", "Linear_15": "Linear_15", "SiLU_21": "SiLU_21", "Dropout_11": "Dropout_11", "Identity_87": "Identity_87", "Linear_16": "Linear_16", "Dropout_12": "Dropout_12", "Identity_88": "Identity_88", "Identity_89": "Identity_89", "__add___10": "__add___10", "Block_5": "Block_5", "LayerNorm_10": "LayerNorm_10", "Attention_5": "Attention_5", "Linear_17": "Linear_17", "reshape_17": "reshape_17", "permute_5": "permute_5", "unbind_5": "unbind_5", "Identity_90": "Identity_90", "Identity_91": "Identity_91", "scaled_dot_product_attention_5": "scaled_dot_product_attention_5", "transpose_11": "transpose_11", "reshape_18": "reshape_18", "Linear_18": "Linear_18", "Dropout_13": "Dropout_13", "Identity_92": "Identity_92", "Identity_93": "Identity_93", "__add___11": "__add___11", "LayerNorm_11": "LayerNorm_11", "Mlp_5": "Mlp_5", "Linear_19": "Linear_19", "SiLU_22": "SiLU_22", "Dropout_14": "Dropout_14", "Identity_94": "Identity_94", "Linear_20": "Linear_20", "Dropout_15": "Dropout_15", "Identity_95": "Identity_95", "Identity_96": "Identity_96", "__add___12": "__add___12", "Block_6": "Block_6", "LayerNorm_12": "LayerNorm_12", "Attention_6": "Attention_6", "Linear_21": "Linear_21", "reshape_19": "reshape_19", "permute_6": "permute_6", "unbind_6": "unbind_6", "Identity_97": "Identity_97", "Identity_98": "Identity_98", "scaled_dot_product_attention_6": "scaled_dot_product_attention_6", "transpose_12": "transpose_12", "reshape_20": "reshape_20", "Linear_22": "Linear_22", "Dropout_16": "Dropout_16", "Identity_99": "Identity_99", "Identity_100": "Identity_100", "__add___13": "__add___13", "LayerNorm_13": "LayerNorm_13", "Mlp_6": "Mlp_6", "Linear_23": "Linear_23", "SiLU_23": "SiLU_23", "Dropout_17": "Dropout_17", "Identity_101": "Identity_101", "Linear_24": "Linear_24", "Dropout_18": "Dropout_18", "Identity_102": "Identity_102", "Identity_103": "Identity_103", "__add___14": "__add___14", "LayerNorm_14": "LayerNorm_14", "contiguous_2": "contiguous_2", "view_2": "view_2", "transpose_13": "transpose_13", "reshape_21": "reshape_21", "transpose_14": "transpose_14", "reshape_22": "reshape_22", "ConvNormAct_24": "ConvNormAct_24", "Conv2d_26": "Conv2d_26", "BatchNormAct2d_24": "BatchNormAct2d_24", "add__24": "add__24", "batch_norm_24": "batch_norm_24", "Identity_104": "Identity_104", "SiLU_24": "SiLU_24", "cat_2": "cat_2", "ConvNormAct_25": "ConvNormAct_25", "Conv2d_27": "Conv2d_27", "BatchNormAct2d_25": "BatchNormAct2d_25", "add__25": "add__25", "batch_norm_25": "batch_norm_25", "Identity_105": "Identity_105", "SiLU_25": "SiLU_25", "Sequential_8": "Sequential_8", "BottleneckBlock_7": "BottleneckBlock_7", "ConvNormAct_26": "ConvNormAct_26", "Conv2d_28": "Conv2d_28", "BatchNormAct2d_26": "BatchNormAct2d_26", "add__26": "add__26", "batch_norm_26": "batch_norm_26", "Identity_106": "Identity_106", "SiLU_26": "SiLU_26", "ConvNormAct_27": "ConvNormAct_27", "Conv2d_29": "Conv2d_29", "BatchNormAct2d_27": "BatchNormAct2d_27", "add__27": "add__27", "batch_norm_27": "batch_norm_27", "Identity_107": "Identity_107", "SiLU_27": "SiLU_27", "Identity_108": "Identity_108", "Identity_109": "Identity_109", "ConvNormAct_28": "ConvNormAct_28", "Conv2d_30": "Conv2d_30", "BatchNormAct2d_28": "BatchNormAct2d_28", "add__28": "add__28", "batch_norm_28": "batch_norm_28", "Identity_110": "Identity_110", "Identity_111": "Identity_111", "Identity_112": "Identity_112", "Identity_113": "Identity_113", "Identity_114": "Identity_114", "MobileVitBlock_3": "MobileVitBlock_3", "ConvNormAct_29": "ConvNormAct_29", "Conv2d_31": "Conv2d_31", "BatchNormAct2d_29": "BatchNormAct2d_29", "add__29": "add__29", "batch_norm_29": "batch_norm_29", "Identity_115": "Identity_115", "SiLU_28": "SiLU_28", "Conv2d_32": "Conv2d_32", "reshape_23": "reshape_23", "transpose_15": "transpose_15", "reshape_24": "reshape_24", "transpose_16": "transpose_16", "reshape_25": "reshape_25", "Sequential_9": "Sequential_9", "Block_7": "Block_7", "LayerNorm_15": "LayerNorm_15", "Attention_7": "Attention_7", "Linear_25": "Linear_25", "reshape_26": "reshape_26", "permute_7": "permute_7", "unbind_7": "unbind_7", "Identity_116": "Identity_116", "Identity_117": "Identity_117", "scaled_dot_product_attention_7": "scaled_dot_product_attention_7", "transpose_17": "transpose_17", "reshape_27": "reshape_27", "Linear_26": "Linear_26", "Dropout_19": "Dropout_19", "Identity_118": "Identity_118", "Identity_119": "Identity_119", "__add___15": "__add___15", "LayerNorm_16": "LayerNorm_16", "Mlp_7": "Mlp_7", "Linear_27": "Linear_27", "SiLU_29": "SiLU_29", "Dropout_20": "Dropout_20", "Identity_120": "Identity_120", "Linear_28": "Linear_28", "Dropout_21": "Dropout_21", "Identity_121": "Identity_121", "Identity_122": "Identity_122", "__add___16": "__add___16", "Block_8": "Block_8", "LayerNorm_17": "LayerNorm_17", "Attention_8": "Attention_8", "Linear_29": "Linear_29", "reshape_28": "reshape_28", "permute_8": "permute_8", "unbind_8": "unbind_8", "Identity_123": "Identity_123", "Identity_124": "Identity_124", "scaled_dot_product_attention_8": "scaled_dot_product_attention_8", "transpose_18": "transpose_18", "reshape_29": "reshape_29", "Linear_30": "Linear_30", "Dropout_22": "Dropout_22", "Identity_125": "Identity_125", "Identity_126": "Identity_126", "__add___17": "__add___17", "LayerNorm_18": "LayerNorm_18", "Mlp_8": "Mlp_8", "Linear_31": "Linear_31", "SiLU_30": "SiLU_30", "Dropout_23": "Dropout_23", "Identity_127": "Identity_127", "Linear_32": "Linear_32", "Dropout_24": "Dropout_24", "Identity_128": "Identity_128", "Identity_129": "Identity_129", "__add___18": "__add___18", "Block_9": "Block_9", "LayerNorm_19": "LayerNorm_19", "Attention_9": "Attention_9", "Linear_33": "Linear_33", "reshape_30": "reshape_30", "permute_9": "permute_9", "unbind_9": "unbind_9", "Identity_130": "Identity_130", "Identity_131": "Identity_131", "scaled_dot_product_attention_9": "scaled_dot_product_attention_9", "transpose_19": "transpose_19", "reshape_31": "reshape_31", "Linear_34": "Linear_34", "Dropout_25": "Dropout_25", "Identity_132": "Identity_132", "Identity_133": "Identity_133", "__add___19": "__add___19", "LayerNorm_20": "LayerNorm_20", "Mlp_9": "Mlp_9", "Linear_35": "Linear_35", "SiLU_31": "SiLU_31", "Dropout_26": "Dropout_26", "Identity_134": "Identity_134", "Linear_36": "Linear_36", "Dropout_27": "Dropout_27", "Identity_135": "Identity_135", "Identity_136": "Identity_136", "__add___20": "__add___20", "LayerNorm_21": "LayerNorm_21", "contiguous_3": "contiguous_3", "view_3": "view_3", "transpose_20": "transpose_20", "reshape_32": "reshape_32", "transpose_21": "transpose_21", "reshape_33": "reshape_33", "ConvNormAct_30": "ConvNormAct_30", "Conv2d_33": "Conv2d_33", "BatchNormAct2d_30": "BatchNormAct2d_30", "add__30": "add__30", "batch_norm_30": "batch_norm_30", "Identity_137": "Identity_137", "SiLU_32": "SiLU_32", "cat_3": "cat_3", "ConvNormAct_31": "ConvNormAct_31", "Conv2d_34": "Conv2d_34", "BatchNormAct2d_31": "BatchNormAct2d_31", "add__31": "add__31", "batch_norm_31": "batch_norm_31", "Identity_138": "Identity_138", "SiLU_33": "SiLU_33", "ConvNormAct_32": "ConvNormAct_32", "Conv2d_35": "Conv2d_35", "BatchNormAct2d_32": "BatchNormAct2d_32", "add__32": "add__32", "batch_norm_32": "batch_norm_32", "Identity_139": "Identity_139", "SiLU_34": "SiLU_34", "ClassifierHead_1": "ClassifierHead_1", "SelectAdaptivePool2d_1": "SelectAdaptivePool2d_1", "AdaptiveAvgPool2d_1": "AdaptiveAvgPool2d_1", "Flatten_1": "Flatten_1", "Dropout_28": "Dropout_28", "Linear_37": "Linear_37", "Identity_140": "Identity_140", "output_0": "output_0"};
    const parent_module_to_nodes = {"ConvNormAct_1": ["Conv2d_1", "add__1", "batch_norm_1", "Identity_1", "SiLU_1"], "BatchNormAct2d_1": ["add__1", "batch_norm_1", "Identity_1", "SiLU_1"], "ConvNormAct_2": ["Conv2d_2", "add__2", "batch_norm_2", "Identity_2", "SiLU_2"], "BottleneckBlock_1": ["Conv2d_2", "add__2", "batch_norm_2", "Identity_2", "SiLU_2", "Conv2d_3", "add__3", "batch_norm_3", "Identity_3", "SiLU_3", "Identity_4", "Identity_5", "Conv2d_4", "add__4", "batch_norm_4", "Identity_6", "Identity_7", "Identity_8", "Identity_9", "Identity_10"], "Sequential_2": ["Conv2d_2", "add__2", "batch_norm_2", "Identity_2", "SiLU_2", "Conv2d_3", "add__3", "batch_norm_3", "Identity_3", "SiLU_3", "Identity_4", "Identity_5", "Conv2d_4", "add__4", "batch_norm_4", "Identity_6", "Identity_7", "Identity_8", "Identity_9", "Identity_10"], "Sequential_1": ["Conv2d_2", "add__2", "batch_norm_2", "Identity_2", "SiLU_2", "Conv2d_3", "add__3", "batch_norm_3", "Identity_3", "SiLU_3", "Identity_4", "Identity_5", "Conv2d_4", "add__4", "batch_norm_4", "Identity_6", "Identity_7", "Identity_8", "Identity_9", "Identity_10", "Conv2d_5", "add__5", "batch_norm_5", "Identity_11", "SiLU_4", "Conv2d_6", "add__6", "batch_norm_6", "Identity_12", "SiLU_5", "Identity_13", "Identity_14", "Conv2d_7", "add__7", "batch_norm_7", "Identity_15", "Identity_16", "Identity_17", "Identity_18", "Identity_19", "Conv2d_8", "add__8", "batch_norm_8", "Identity_20", "SiLU_6", "Conv2d_9", "add__9", "batch_norm_9", "Identity_21", "SiLU_7", "Identity_22", "Identity_23", "Conv2d_10", "add__10", "batch_norm_10", "Identity_24", "Identity_25", "Identity_26", "Identity_27", "Identity_28", "__add___1", "Identity_29", "Conv2d_11", "add__11", "batch_norm_11", "Identity_30", "SiLU_8", "Conv2d_12", "add__12", "batch_norm_12", "Identity_31", "SiLU_9", "Identity_32", "Identity_33", "Conv2d_13", "add__13", "batch_norm_13", "Identity_34", "Identity_35", "Identity_36", "Identity_37", "Identity_38", "__add___2", "Identity_39", "Conv2d_14", "add__14", "batch_norm_14", "Identity_40", "SiLU_10", "Conv2d_15", "add__15", "batch_norm_15", "Identity_41", "SiLU_11", "Identity_42", "Identity_43", "Conv2d_16", "add__16", "batch_norm_16", "Identity_44", "Identity_45", "Identity_46", "Identity_47", "Identity_48", "Conv2d_17", "add__17", "batch_norm_17", "Identity_49", "SiLU_12", "Conv2d_18", "reshape_1", "transpose_1", "reshape_2", "transpose_2", "reshape_3", "LayerNorm_1", "Linear_1", "reshape_4", "permute_1", "unbind_1", "Identity_50", "Identity_51", "scaled_dot_product_attention_1", "transpose_3", "reshape_5", "Linear_2", "Dropout_1", "Identity_52", "Identity_53", "__add___3", "LayerNorm_2", "Linear_3", "SiLU_13", "Dropout_2", "Identity_54", "Linear_4", "Dropout_3", "Identity_55", "Identity_56", "__add___4", "LayerNorm_3", "Linear_5", "reshape_6", "permute_2", "unbind_2", "Identity_57", "Identity_58", "scaled_dot_product_attention_2", "transpose_4", "reshape_7", "Linear_6", "Dropout_4", "Identity_59", "Identity_60", "__add___5", "LayerNorm_4", "Linear_7", "SiLU_14", "Dropout_5", "Identity_61", "Linear_8", "Dropout_6", "Identity_62", "Identity_63", "__add___6", "LayerNorm_5", "contiguous_1", "view_1", "transpose_5", "reshape_8", "transpose_6", "reshape_9", "Conv2d_19", "add__18", "batch_norm_18", "Identity_64", "SiLU_15", "cat_1", "Conv2d_20", "add__19", "batch_norm_19", "Identity_65", "SiLU_16", "Conv2d_21", "add__20", "batch_norm_20", "Identity_66", "SiLU_17", "Conv2d_22", "add__21", "batch_norm_21", "Identity_67", "SiLU_18", "Identity_68", "Identity_69", "Conv2d_23", "add__22", "batch_norm_22", "Identity_70", "Identity_71", "Identity_72", "Identity_73", "Identity_74", "Conv2d_24", "add__23", "batch_norm_23", "Identity_75", "SiLU_19", "Conv2d_25", "reshape_10", "transpose_7", "reshape_11", "transpose_8", "reshape_12", "LayerNorm_6", "Linear_9", "reshape_13", "permute_3", "unbind_3", "Identity_76", "Identity_77", "scaled_dot_product_attention_3", "transpose_9", "reshape_14", "Linear_10", "Dropout_7", "Identity_78", "Identity_79", "__add___7", "LayerNorm_7", "Linear_11", "SiLU_20", "Dropout_8", "Identity_80", "Linear_12", "Dropout_9", "Identity_81", "Identity_82", "__add___8", "LayerNorm_8", "Linear_13", "reshape_15", "permute_4", "unbind_4", "Identity_83", "Identity_84", "scaled_dot_product_attention_4", "transpose_10", "reshape_16", "Linear_14", "Dropout_10", "Identity_85", "Identity_86", "__add___9", "LayerNorm_9", "Linear_15", "SiLU_21", "Dropout_11", "Identity_87", "Linear_16", "Dropout_12", "Identity_88", "Identity_89", "__add___10", "LayerNorm_10", "Linear_17", "reshape_17", "permute_5", "unbind_5", "Identity_90", "Identity_91", "scaled_dot_product_attention_5", "transpose_11", "reshape_18", "Linear_18", "Dropout_13", "Identity_92", "Identity_93", "__add___11", "LayerNorm_11", "Linear_19", "SiLU_22", "Dropout_14", "Identity_94", "Linear_20", "Dropout_15", "Identity_95", "Identity_96", "__add___12", "LayerNorm_12", "Linear_21", "reshape_19", "permute_6", "unbind_6", "Identity_97", "Identity_98", "scaled_dot_product_attention_6", "transpose_12", "reshape_20", "Linear_22", "Dropout_16", "Identity_99", "Identity_100", "__add___13", "LayerNorm_13", "Linear_23", "SiLU_23", "Dropout_17", "Identity_101", "Linear_24", "Dropout_18", "Identity_102", "Identity_103", "__add___14", "LayerNorm_14", "contiguous_2", "view_2", "transpose_13", "reshape_21", "transpose_14", "reshape_22", "Conv2d_26", "add__24", "batch_norm_24", "Identity_104", "SiLU_24", "cat_2", "Conv2d_27", "add__25", "batch_norm_25", "Identity_105", "SiLU_25", "Conv2d_28", "add__26", "batch_norm_26", "Identity_106", "SiLU_26", "Conv2d_29", "add__27", "batch_norm_27", "Identity_107", "SiLU_27", "Identity_108", "Identity_109", "Conv2d_30", "add__28", "batch_norm_28", "Identity_110", "Identity_111", "Identity_112", "Identity_113", "Identity_114", "Conv2d_31", "add__29", "batch_norm_29", "Identity_115", "SiLU_28", "Conv2d_32", "reshape_23", "transpose_15", "reshape_24", "transpose_16", "reshape_25", "LayerNorm_15", "Linear_25", "reshape_26", "permute_7", "unbind_7", "Identity_116", "Identity_117", "scaled_dot_product_attention_7", "transpose_17", "reshape_27", "Linear_26", "Dropout_19", "Identity_118", "Identity_119", "__add___15", "LayerNorm_16", "Linear_27", "SiLU_29", "Dropout_20", "Identity_120", "Linear_28", "Dropout_21", "Identity_121", "Identity_122", "__add___16", "LayerNorm_17", "Linear_29", "reshape_28", "permute_8", "unbind_8", "Identity_123", "Identity_124", "scaled_dot_product_attention_8", "transpose_18", "reshape_29", "Linear_30", "Dropout_22", "Identity_125", "Identity_126", "__add___17", "LayerNorm_18", "Linear_31", "SiLU_30", "Dropout_23", "Identity_127", "Linear_32", "Dropout_24", "Identity_128", "Identity_129", "__add___18", "LayerNorm_19", "Linear_33", "reshape_30", "permute_9", "unbind_9", "Identity_130", "Identity_131", "scaled_dot_product_attention_9", "transpose_19", "reshape_31", "Linear_34", "Dropout_25", "Identity_132", "Identity_133", "__add___19", "LayerNorm_20", "Linear_35", "SiLU_31", "Dropout_26", "Identity_134", "Linear_36", "Dropout_27", "Identity_135", "Identity_136", "__add___20", "LayerNorm_21", "contiguous_3", "view_3", "transpose_20", "reshape_32", "transpose_21", "reshape_33", "Conv2d_33", "add__30", "batch_norm_30", "Identity_137", "SiLU_32", "cat_3", "Conv2d_34", "add__31", "batch_norm_31", "Identity_138", "SiLU_33"], "BatchNormAct2d_2": ["add__2", "batch_norm_2", "Identity_2", "SiLU_2"], "ConvNormAct_3": ["Conv2d_3", "add__3", "batch_norm_3", "Identity_3", "SiLU_3"], "BatchNormAct2d_3": ["add__3", "batch_norm_3", "Identity_3", "SiLU_3"], "ConvNormAct_4": ["Conv2d_4", "add__4", "batch_norm_4", "Identity_6", "Identity_7"], "BatchNormAct2d_4": ["add__4", "batch_norm_4", "Identity_6", "Identity_7"], "ConvNormAct_5": ["Conv2d_5", "add__5", "batch_norm_5", "Identity_11", "SiLU_4"], "BottleneckBlock_2": ["Conv2d_5", "add__5", "batch_norm_5", "Identity_11", "SiLU_4", "Conv2d_6", "add__6", "batch_norm_6", "Identity_12", "SiLU_5", "Identity_13", "Identity_14", "Conv2d_7", "add__7", "batch_norm_7", "Identity_15", "Identity_16", "Identity_17", "Identity_18", "Identity_19"], "Sequential_3": ["Conv2d_5", "add__5", "batch_norm_5", "Identity_11", "SiLU_4", "Conv2d_6", "add__6", "batch_norm_6", "Identity_12", "SiLU_5", "Identity_13", "Identity_14", "Conv2d_7", "add__7", "batch_norm_7", "Identity_15", "Identity_16", "Identity_17", "Identity_18", "Identity_19", "Conv2d_8", "add__8", "batch_norm_8", "Identity_20", "SiLU_6", "Conv2d_9", "add__9", "batch_norm_9", "Identity_21", "SiLU_7", "Identity_22", "Identity_23", "Conv2d_10", "add__10", "batch_norm_10", "Identity_24", "Identity_25", "Identity_26", "Identity_27", "Identity_28", "__add___1", "Identity_29", "Conv2d_11", "add__11", "batch_norm_11", "Identity_30", "SiLU_8", "Conv2d_12", "add__12", "batch_norm_12", "Identity_31", "SiLU_9", "Identity_32", "Identity_33", "Conv2d_13", "add__13", "batch_norm_13", "Identity_34", "Identity_35", "Identity_36", "Identity_37", "Identity_38", "__add___2", "Identity_39"], "BatchNormAct2d_5": ["add__5", "batch_norm_5", "Identity_11", "SiLU_4"], "ConvNormAct_6": ["Conv2d_6", "add__6", "batch_norm_6", "Identity_12", "SiLU_5"], "BatchNormAct2d_6": ["add__6", "batch_norm_6", "Identity_12", "SiLU_5"], "ConvNormAct_7": ["Conv2d_7", "add__7", "batch_norm_7", "Identity_15", "Identity_16"], "BatchNormAct2d_7": ["add__7", "batch_norm_7", "Identity_15", "Identity_16"], "ConvNormAct_8": ["Conv2d_8", "add__8", "batch_norm_8", "Identity_20", "SiLU_6"], "BottleneckBlock_3": ["Conv2d_8", "add__8", "batch_norm_8", "Identity_20", "SiLU_6", "Conv2d_9", "add__9", "batch_norm_9", "Identity_21", "SiLU_7", "Identity_22", "Identity_23", "Conv2d_10", "add__10", "batch_norm_10", "Identity_24", "Identity_25", "Identity_26", "Identity_27", "Identity_28", "__add___1", "Identity_29"], "BatchNormAct2d_8": ["add__8", "batch_norm_8", "Identity_20", "SiLU_6"], "ConvNormAct_9": ["Conv2d_9", "add__9", "batch_norm_9", 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["Linear_21", "reshape_19", "permute_6", "unbind_6", "Identity_97", "Identity_98", "scaled_dot_product_attention_6", "transpose_12", "reshape_20", "Linear_22", "Dropout_16"], "Mlp_6": ["Linear_23", "SiLU_23", "Dropout_17", "Identity_101", "Linear_24", "Dropout_18"], "ConvNormAct_24": ["Conv2d_26", "add__24", "batch_norm_24", "Identity_104", "SiLU_24"], "BatchNormAct2d_24": ["add__24", "batch_norm_24", "Identity_104", "SiLU_24"], "ConvNormAct_25": ["Conv2d_27", "add__25", "batch_norm_25", "Identity_105", "SiLU_25"], "BatchNormAct2d_25": ["add__25", "batch_norm_25", "Identity_105", "SiLU_25"], "ConvNormAct_26": ["Conv2d_28", "add__26", "batch_norm_26", "Identity_106", "SiLU_26"], "BottleneckBlock_7": ["Conv2d_28", "add__26", "batch_norm_26", "Identity_106", "SiLU_26", "Conv2d_29", "add__27", "batch_norm_27", "Identity_107", "SiLU_27", "Identity_108", "Identity_109", "Conv2d_30", "add__28", "batch_norm_28", "Identity_110", "Identity_111", "Identity_112", "Identity_113", "Identity_114"], "Sequential_8": ["Conv2d_28", "add__26", "batch_norm_26", "Identity_106", "SiLU_26", "Conv2d_29", "add__27", "batch_norm_27", "Identity_107", "SiLU_27", "Identity_108", "Identity_109", "Conv2d_30", "add__28", "batch_norm_28", "Identity_110", "Identity_111", "Identity_112", "Identity_113", "Identity_114", "Conv2d_31", "add__29", "batch_norm_29", "Identity_115", "SiLU_28", "Conv2d_32", "reshape_23", "transpose_15", "reshape_24", "transpose_16", "reshape_25", "LayerNorm_15", "Linear_25", "reshape_26", "permute_7", "unbind_7", "Identity_116", "Identity_117", "scaled_dot_product_attention_7", "transpose_17", "reshape_27", "Linear_26", "Dropout_19", "Identity_118", "Identity_119", "__add___15", "LayerNorm_16", "Linear_27", "SiLU_29", "Dropout_20", "Identity_120", "Linear_28", "Dropout_21", "Identity_121", "Identity_122", "__add___16", "LayerNorm_17", "Linear_29", "reshape_28", "permute_8", "unbind_8", "Identity_123", "Identity_124", "scaled_dot_product_attention_8", "transpose_18", "reshape_29", "Linear_30", "Dropout_22", "Identity_125", "Identity_126", "__add___17", "LayerNorm_18", "Linear_31", "SiLU_30", "Dropout_23", "Identity_127", "Linear_32", "Dropout_24", "Identity_128", "Identity_129", "__add___18", "LayerNorm_19", "Linear_33", "reshape_30", "permute_9", "unbind_9", "Identity_130", "Identity_131", "scaled_dot_product_attention_9", "transpose_19", "reshape_31", "Linear_34", "Dropout_25", "Identity_132", "Identity_133", "__add___19", "LayerNorm_20", "Linear_35", "SiLU_31", "Dropout_26", "Identity_134", "Linear_36", "Dropout_27", "Identity_135", "Identity_136", "__add___20", "LayerNorm_21", "contiguous_3", "view_3", "transpose_20", "reshape_32", "transpose_21", "reshape_33", "Conv2d_33", "add__30", "batch_norm_30", "Identity_137", "SiLU_32", "cat_3", "Conv2d_34", "add__31", "batch_norm_31", "Identity_138", "SiLU_33"], "BatchNormAct2d_26": ["add__26", "batch_norm_26", "Identity_106", "SiLU_26"], "ConvNormAct_27": ["Conv2d_29", "add__27", "batch_norm_27", "Identity_107", "SiLU_27"], "BatchNormAct2d_27": ["add__27", "batch_norm_27", "Identity_107", "SiLU_27"], "ConvNormAct_28": ["Conv2d_30", "add__28", "batch_norm_28", "Identity_110", "Identity_111"], "BatchNormAct2d_28": ["add__28", "batch_norm_28", "Identity_110", "Identity_111"], "ConvNormAct_29": ["Conv2d_31", "add__29", "batch_norm_29", "Identity_115", "SiLU_28"], "MobileVitBlock_3": ["Conv2d_31", "add__29", "batch_norm_29", "Identity_115", "SiLU_28", "Conv2d_32", "reshape_23", "transpose_15", "reshape_24", "transpose_16", "reshape_25", "LayerNorm_15", "Linear_25", "reshape_26", "permute_7", "unbind_7", "Identity_116", "Identity_117", "scaled_dot_product_attention_7", "transpose_17", "reshape_27", "Linear_26", "Dropout_19", "Identity_118", "Identity_119", "__add___15", "LayerNorm_16", "Linear_27", "SiLU_29", "Dropout_20", "Identity_120", "Linear_28", "Dropout_21", "Identity_121", "Identity_122", "__add___16", "LayerNorm_17", "Linear_29", "reshape_28", "permute_8", "unbind_8", "Identity_123", "Identity_124", "scaled_dot_product_attention_8", "transpose_18", "reshape_29", "Linear_30", "Dropout_22", "Identity_125", "Identity_126", "__add___17", "LayerNorm_18", "Linear_31", "SiLU_30", "Dropout_23", "Identity_127", "Linear_32", "Dropout_24", "Identity_128", "Identity_129", "__add___18", "LayerNorm_19", "Linear_33", "reshape_30", "permute_9", "unbind_9", "Identity_130", "Identity_131", "scaled_dot_product_attention_9", "transpose_19", "reshape_31", "Linear_34", "Dropout_25", "Identity_132", "Identity_133", "__add___19", "LayerNorm_20", "Linear_35", "SiLU_31", "Dropout_26", "Identity_134", "Linear_36", "Dropout_27", "Identity_135", "Identity_136", "__add___20", "LayerNorm_21", "contiguous_3", "view_3", "transpose_20", "reshape_32", "transpose_21", "reshape_33", "Conv2d_33", "add__30", "batch_norm_30", "Identity_137", "SiLU_32", "cat_3", "Conv2d_34", "add__31", "batch_norm_31", "Identity_138", "SiLU_33"], "BatchNormAct2d_29": ["add__29", "batch_norm_29", "Identity_115", "SiLU_28"], "Block_7": ["LayerNorm_15", "Linear_25", "reshape_26", "permute_7", "unbind_7", "Identity_116", "Identity_117", "scaled_dot_product_attention_7", "transpose_17", "reshape_27", "Linear_26", "Dropout_19", "Identity_118", "Identity_119", "__add___15", "LayerNorm_16", "Linear_27", "SiLU_29", "Dropout_20", "Identity_120", "Linear_28", "Dropout_21", "Identity_121", "Identity_122", "__add___16"], "Sequential_9": ["LayerNorm_15", "Linear_25", "reshape_26", "permute_7", "unbind_7", "Identity_116", "Identity_117", "scaled_dot_product_attention_7", "transpose_17", "reshape_27", "Linear_26", "Dropout_19", "Identity_118", "Identity_119", "__add___15", "LayerNorm_16", "Linear_27", "SiLU_29", "Dropout_20", "Identity_120", "Linear_28", "Dropout_21", "Identity_121", "Identity_122", "__add___16", "LayerNorm_17", "Linear_29", "reshape_28", "permute_8", "unbind_8", "Identity_123", "Identity_124", "scaled_dot_product_attention_8", "transpose_18", "reshape_29", "Linear_30", "Dropout_22", "Identity_125", "Identity_126", "__add___17", "LayerNorm_18", "Linear_31", "SiLU_30", "Dropout_23", "Identity_127", "Linear_32", "Dropout_24", "Identity_128", "Identity_129", "__add___18", "LayerNorm_19", "Linear_33", "reshape_30", "permute_9", "unbind_9", "Identity_130", "Identity_131", "scaled_dot_product_attention_9", "transpose_19", "reshape_31", "Linear_34", "Dropout_25", "Identity_132", "Identity_133", "__add___19", "LayerNorm_20", "Linear_35", "SiLU_31", "Dropout_26", "Identity_134", "Linear_36", "Dropout_27", "Identity_135", "Identity_136", "__add___20"], "Attention_7": ["Linear_25", "reshape_26", "permute_7", "unbind_7", "Identity_116", "Identity_117", "scaled_dot_product_attention_7", "transpose_17", "reshape_27", "Linear_26", "Dropout_19"], "Mlp_7": ["Linear_27", "SiLU_29", "Dropout_20", "Identity_120", "Linear_28", "Dropout_21"], "Block_8": ["LayerNorm_17", "Linear_29", "reshape_28", "permute_8", "unbind_8", "Identity_123", "Identity_124", "scaled_dot_product_attention_8", "transpose_18", "reshape_29", "Linear_30", "Dropout_22", "Identity_125", "Identity_126", "__add___17", "LayerNorm_18", "Linear_31", "SiLU_30", "Dropout_23", "Identity_127", "Linear_32", "Dropout_24", "Identity_128", "Identity_129", "__add___18"], "Attention_8": ["Linear_29", "reshape_28", "permute_8", "unbind_8", "Identity_123", "Identity_124", "scaled_dot_product_attention_8", "transpose_18", "reshape_29", "Linear_30", "Dropout_22"], "Mlp_8": ["Linear_31", "SiLU_30", "Dropout_23", "Identity_127", "Linear_32", "Dropout_24"], "Block_9": ["LayerNorm_19", "Linear_33", "reshape_30", "permute_9", "unbind_9", "Identity_130", "Identity_131", "scaled_dot_product_attention_9", "transpose_19", "reshape_31", "Linear_34", "Dropout_25", "Identity_132", "Identity_133", "__add___19", "LayerNorm_20", "Linear_35", "SiLU_31", "Dropout_26", "Identity_134", "Linear_36", "Dropout_27", "Identity_135", "Identity_136", "__add___20"], "Attention_9": ["Linear_33", "reshape_30", "permute_9", "unbind_9", "Identity_130", "Identity_131", "scaled_dot_product_attention_9", "transpose_19", "reshape_31", "Linear_34", "Dropout_25"], "Mlp_9": ["Linear_35", "SiLU_31", "Dropout_26", "Identity_134", "Linear_36", "Dropout_27"], "ConvNormAct_30": ["Conv2d_33", "add__30", "batch_norm_30", "Identity_137", "SiLU_32"], "BatchNormAct2d_30": ["add__30", "batch_norm_30", "Identity_137", "SiLU_32"], "ConvNormAct_31": ["Conv2d_34", "add__31", "batch_norm_31", "Identity_138", "SiLU_33"], "BatchNormAct2d_31": ["add__31", "batch_norm_31", "Identity_138", "SiLU_33"], "ConvNormAct_32": ["Conv2d_35", "add__32", "batch_norm_32", "Identity_139", "SiLU_34"], "BatchNormAct2d_32": ["add__32", "batch_norm_32", "Identity_139", "SiLU_34"], "SelectAdaptivePool2d_1": ["AdaptiveAvgPool2d_1", "Flatten_1"], "ClassifierHead_1": ["AdaptiveAvgPool2d_1", "Flatten_1", "Dropout_28", "Linear_37", "Identity_140"]};
    const parent_module_to_depth = {"ConvNormAct_1": 2, "BatchNormAct2d_1": 1, "ConvNormAct_2": 2, "BottleneckBlock_1": 3, "Sequential_2": 4, "Sequential_1": 6, "BatchNormAct2d_2": 1, "ConvNormAct_3": 2, "BatchNormAct2d_3": 1, "ConvNormAct_4": 2, "BatchNormAct2d_4": 1, "ConvNormAct_5": 2, "BottleneckBlock_2": 3, "Sequential_3": 4, "BatchNormAct2d_5": 1, "ConvNormAct_6": 2, "BatchNormAct2d_6": 1, "ConvNormAct_7": 2, "BatchNormAct2d_7": 1, "ConvNormAct_8": 2, "BottleneckBlock_3": 3, "BatchNormAct2d_8": 1, "ConvNormAct_9": 2, "BatchNormAct2d_9": 1, "ConvNormAct_10": 2, "BatchNormAct2d_10": 1, "ConvNormAct_11": 2, "BottleneckBlock_4": 3, "BatchNormAct2d_11": 1, "ConvNormAct_12": 2, "BatchNormAct2d_12": 1, "ConvNormAct_13": 2, "BatchNormAct2d_13": 1, "ConvNormAct_14": 2, "BottleneckBlock_5": 3, "Sequential_4": 5, "BatchNormAct2d_14": 1, "ConvNormAct_15": 2, "BatchNormAct2d_15": 1, "ConvNormAct_16": 2, "BatchNormAct2d_16": 1, "ConvNormAct_17": 2, "MobileVitBlock_1": 4, "BatchNormAct2d_17": 1, "Block_1": 2, "Sequential_5": 3, "Attention_1": 1, "Mlp_1": 1, "Block_2": 2, "Attention_2": 1, "Mlp_2": 1, "ConvNormAct_18": 2, "BatchNormAct2d_18": 1, "ConvNormAct_19": 2, "BatchNormAct2d_19": 1, "ConvNormAct_20": 2, "BottleneckBlock_6": 3, "Sequential_6": 5, "BatchNormAct2d_20": 1, "ConvNormAct_21": 2, "BatchNormAct2d_21": 1, "ConvNormAct_22": 2, "BatchNormAct2d_22": 1, "ConvNormAct_23": 2, "MobileVitBlock_2": 4, "BatchNormAct2d_23": 1, "Block_3": 2, "Sequential_7": 3, "Attention_3": 1, "Mlp_3": 1, "Block_4": 2, "Attention_4": 1, "Mlp_4": 1, "Block_5": 2, "Attention_5": 1, "Mlp_5": 1, "Block_6": 2, "Attention_6": 1, "Mlp_6": 1, "ConvNormAct_24": 2, "BatchNormAct2d_24": 1, "ConvNormAct_25": 2, "BatchNormAct2d_25": 1, "ConvNormAct_26": 2, "BottleneckBlock_7": 3, "Sequential_8": 5, "BatchNormAct2d_26": 1, "ConvNormAct_27": 2, "BatchNormAct2d_27": 1, "ConvNormAct_28": 2, "BatchNormAct2d_28": 1, "ConvNormAct_29": 2, "MobileVitBlock_3": 4, "BatchNormAct2d_29": 1, "Block_7": 2, "Sequential_9": 3, "Attention_7": 1, "Mlp_7": 1, "Block_8": 2, "Attention_8": 1, "Mlp_8": 1, "Block_9": 2, "Attention_9": 1, "Mlp_9": 1, "ConvNormAct_30": 2, "BatchNormAct2d_30": 1, "ConvNormAct_31": 2, "BatchNormAct2d_31": 1, "ConvNormAct_32": 2, "BatchNormAct2d_32": 1, "SelectAdaptivePool2d_1": 1, "ClassifierHead_1": 2};
    const graph_node_name_to_without_suffix = {"input_0": "input_0", "Conv2d_1": "Conv2d", "add__1": "add_", "batch_norm_1": "batch_norm", "Identity_1": "Identity", "SiLU_1": "SiLU", "Conv2d_2": "Conv2d", "add__2": "add_", "batch_norm_2": "batch_norm", "Identity_2": "Identity", "SiLU_2": "SiLU", "Conv2d_3": "Conv2d", "add__3": "add_", "batch_norm_3": "batch_norm", "Identity_3": "Identity", "SiLU_3": "SiLU", "Identity_4": "Identity", "Identity_5": "Identity", "Conv2d_4": "Conv2d", "add__4": "add_", "batch_norm_4": "batch_norm", "Identity_6": "Identity", "Identity_7": "Identity", "Identity_8": "Identity", "Identity_9": "Identity", "Identity_10": "Identity", "Conv2d_5": "Conv2d", "add__5": "add_", "batch_norm_5": "batch_norm", "Identity_11": "Identity", "SiLU_4": "SiLU", "Conv2d_6": "Conv2d", "add__6": "add_", "batch_norm_6": "batch_norm", "Identity_12": "Identity", "SiLU_5": "SiLU", "Identity_13": "Identity", "Identity_14": "Identity", "Conv2d_7": "Conv2d", "add__7": "add_", "batch_norm_7": "batch_norm", "Identity_15": "Identity", "Identity_16": "Identity", "Identity_17": "Identity", "Identity_18": "Identity", "Identity_19": "Identity", "Conv2d_8": "Conv2d", "add__8": "add_", "batch_norm_8": "batch_norm", "Identity_20": "Identity", "SiLU_6": "SiLU", "Conv2d_9": "Conv2d", "add__9": "add_", "batch_norm_9": "batch_norm", "Identity_21": "Identity", "SiLU_7": "SiLU", "Identity_22": "Identity", "Identity_23": "Identity", "Conv2d_10": "Conv2d", "add__10": "add_", "batch_norm_10": "batch_norm", "Identity_24": "Identity", "Identity_25": "Identity", "Identity_26": "Identity", "Identity_27": "Identity", "Identity_28": "Identity", "__add___1": "__add__", "Identity_29": "Identity", "Conv2d_11": "Conv2d", "add__11": "add_", "batch_norm_11": "batch_norm", "Identity_30": "Identity", "SiLU_8": "SiLU", "Conv2d_12": "Conv2d", "add__12": "add_", "batch_norm_12": "batch_norm", "Identity_31": "Identity", "SiLU_9": "SiLU", "Identity_32": "Identity", "Identity_33": "Identity", "Conv2d_13": "Conv2d", "add__13": "add_", "batch_norm_13": "batch_norm", "Identity_34": "Identity", "Identity_35": "Identity", "Identity_36": "Identity", "Identity_37": "Identity", "Identity_38": "Identity", "__add___2": "__add__", "Identity_39": "Identity", "Conv2d_14": "Conv2d", "add__14": "add_", "batch_norm_14": "batch_norm", "Identity_40": "Identity", "SiLU_10": "SiLU", "Conv2d_15": "Conv2d", "add__15": "add_", "batch_norm_15": "batch_norm", "Identity_41": "Identity", "SiLU_11": "SiLU", "Identity_42": "Identity", "Identity_43": "Identity", "Conv2d_16": "Conv2d", "add__16": "add_", "batch_norm_16": "batch_norm", "Identity_44": "Identity", "Identity_45": "Identity", "Identity_46": "Identity", "Identity_47": "Identity", "Identity_48": "Identity", "Conv2d_17": "Conv2d", "add__17": "add_", "batch_norm_17": "batch_norm", "Identity_49": "Identity", "SiLU_12": "SiLU", "Conv2d_18": "Conv2d", "reshape_1": "reshape", "transpose_1": "transpose", "reshape_2": "reshape", "transpose_2": "transpose", "reshape_3": "reshape", "LayerNorm_1": "LayerNorm", "Linear_1": "Linear", "reshape_4": "reshape", "permute_1": "permute", "unbind_1": "unbind", "Identity_50": "Identity", "Identity_51": "Identity", "scaled_dot_product_attention_1": "scaled_dot_product_attention", "transpose_3": "transpose", "reshape_5": "reshape", "Linear_2": "Linear", "Dropout_1": "Dropout", "Identity_52": "Identity", "Identity_53": "Identity", "__add___3": "__add__", "LayerNorm_2": "LayerNorm", "Linear_3": "Linear", "SiLU_13": "SiLU", "Dropout_2": "Dropout", "Identity_54": "Identity", "Linear_4": "Linear", "Dropout_3": "Dropout", "Identity_55": "Identity", "Identity_56": "Identity", "__add___4": "__add__", "LayerNorm_3": "LayerNorm", "Linear_5": "Linear", "reshape_6": "reshape", "permute_2": "permute", "unbind_2": "unbind", "Identity_57": "Identity", "Identity_58": "Identity", "scaled_dot_product_attention_2": "scaled_dot_product_attention", "transpose_4": "transpose", "reshape_7": "reshape", "Linear_6": "Linear", "Dropout_4": "Dropout", "Identity_59": "Identity", "Identity_60": "Identity", "__add___5": "__add__", "LayerNorm_4": "LayerNorm", "Linear_7": "Linear", "SiLU_14": "SiLU", "Dropout_5": "Dropout", "Identity_61": "Identity", "Linear_8": "Linear", "Dropout_6": "Dropout", "Identity_62": "Identity", "Identity_63": "Identity", "__add___6": "__add__", "LayerNorm_5": "LayerNorm", "contiguous_1": "contiguous", "view_1": "view", "transpose_5": "transpose", "reshape_8": "reshape", "transpose_6": "transpose", "reshape_9": "reshape", "Conv2d_19": "Conv2d", "add__18": "add_", "batch_norm_18": "batch_norm", "Identity_64": "Identity", "SiLU_15": "SiLU", "cat_1": "cat", "Conv2d_20": "Conv2d", "add__19": "add_", "batch_norm_19": "batch_norm", "Identity_65": "Identity", "SiLU_16": "SiLU", "Conv2d_21": "Conv2d", "add__20": "add_", "batch_norm_20": "batch_norm", "Identity_66": "Identity", "SiLU_17": "SiLU", "Conv2d_22": "Conv2d", "add__21": "add_", "batch_norm_21": "batch_norm", "Identity_67": "Identity", "SiLU_18": "SiLU", "Identity_68": "Identity", "Identity_69": "Identity", "Conv2d_23": "Conv2d", "add__22": "add_", "batch_norm_22": "batch_norm", "Identity_70": "Identity", "Identity_71": "Identity", "Identity_72": "Identity", "Identity_73": "Identity", "Identity_74": "Identity", "Conv2d_24": "Conv2d", "add__23": "add_", "batch_norm_23": "batch_norm", "Identity_75": "Identity", "SiLU_19": "SiLU", "Conv2d_25": "Conv2d", "reshape_10": "reshape", "transpose_7": "transpose", "reshape_11": "reshape", "transpose_8": "transpose", "reshape_12": "reshape", "LayerNorm_6": "LayerNorm", "Linear_9": "Linear", "reshape_13": "reshape", "permute_3": "permute", "unbind_3": "unbind", "Identity_76": "Identity", "Identity_77": "Identity", "scaled_dot_product_attention_3": "scaled_dot_product_attention", "transpose_9": "transpose", "reshape_14": "reshape", "Linear_10": "Linear", "Dropout_7": "Dropout", "Identity_78": "Identity", "Identity_79": "Identity", "__add___7": "__add__", "LayerNorm_7": "LayerNorm", "Linear_11": "Linear", "SiLU_20": "SiLU", "Dropout_8": "Dropout", "Identity_80": "Identity", "Linear_12": "Linear", "Dropout_9": "Dropout", "Identity_81": "Identity", "Identity_82": "Identity", "__add___8": "__add__", "LayerNorm_8": "LayerNorm", "Linear_13": "Linear", "reshape_15": "reshape", "permute_4": "permute", "unbind_4": "unbind", "Identity_83": "Identity", "Identity_84": "Identity", "scaled_dot_product_attention_4": "scaled_dot_product_attention", "transpose_10": "transpose", "reshape_16": "reshape", "Linear_14": "Linear", "Dropout_10": "Dropout", "Identity_85": "Identity", "Identity_86": "Identity", "__add___9": "__add__", "LayerNorm_9": "LayerNorm", "Linear_15": "Linear", "SiLU_21": "SiLU", "Dropout_11": "Dropout", "Identity_87": "Identity", "Linear_16": "Linear", "Dropout_12": "Dropout", "Identity_88": "Identity", "Identity_89": "Identity", "__add___10": "__add__", "LayerNorm_10": "LayerNorm", "Linear_17": "Linear", "reshape_17": "reshape", "permute_5": "permute", "unbind_5": "unbind", "Identity_90": "Identity", "Identity_91": "Identity", "scaled_dot_product_attention_5": "scaled_dot_product_attention", "transpose_11": "transpose", "reshape_18": "reshape", "Linear_18": "Linear", "Dropout_13": "Dropout", "Identity_92": "Identity", "Identity_93": "Identity", "__add___11": "__add__", "LayerNorm_11": "LayerNorm", "Linear_19": "Linear", "SiLU_22": "SiLU", "Dropout_14": "Dropout", "Identity_94": "Identity", "Linear_20": "Linear", "Dropout_15": "Dropout", "Identity_95": "Identity", "Identity_96": "Identity", "__add___12": "__add__", "LayerNorm_12": "LayerNorm", "Linear_21": "Linear", "reshape_19": "reshape", "permute_6": "permute", "unbind_6": "unbind", "Identity_97": "Identity", "Identity_98": "Identity", "scaled_dot_product_attention_6": "scaled_dot_product_attention", "transpose_12": "transpose", "reshape_20": "reshape", "Linear_22": "Linear", "Dropout_16": "Dropout", "Identity_99": "Identity", "Identity_100": "Identity", "__add___13": "__add__", "LayerNorm_13": "LayerNorm", "Linear_23": "Linear", "SiLU_23": "SiLU", "Dropout_17": "Dropout", "Identity_101": "Identity", "Linear_24": "Linear", "Dropout_18": "Dropout", "Identity_102": "Identity", "Identity_103": "Identity", "__add___14": "__add__", "LayerNorm_14": "LayerNorm", "contiguous_2": "contiguous", "view_2": "view", "transpose_13": "transpose", "reshape_21": "reshape", "transpose_14": "transpose", "reshape_22": "reshape", "Conv2d_26": "Conv2d", "add__24": "add_", "batch_norm_24": "batch_norm", "Identity_104": "Identity", "SiLU_24": "SiLU", "cat_2": "cat", "Conv2d_27": "Conv2d", "add__25": "add_", "batch_norm_25": "batch_norm", "Identity_105": "Identity", "SiLU_25": "SiLU", "Conv2d_28": "Conv2d", "add__26": "add_", "batch_norm_26": "batch_norm", "Identity_106": "Identity", "SiLU_26": "SiLU", "Conv2d_29": "Conv2d", "add__27": "add_", "batch_norm_27": "batch_norm", "Identity_107": "Identity", "SiLU_27": "SiLU", "Identity_108": "Identity", "Identity_109": "Identity", "Conv2d_30": "Conv2d", "add__28": "add_", "batch_norm_28": "batch_norm", "Identity_110": "Identity", "Identity_111": "Identity", "Identity_112": "Identity", "Identity_113": "Identity", "Identity_114": "Identity", "Conv2d_31": "Conv2d", "add__29": "add_", "batch_norm_29": "batch_norm", "Identity_115": "Identity", "SiLU_28": "SiLU", "Conv2d_32": "Conv2d", "reshape_23": "reshape", "transpose_15": "transpose", "reshape_24": "reshape", "transpose_16": "transpose", "reshape_25": "reshape", "LayerNorm_15": "LayerNorm", "Linear_25": "Linear", "reshape_26": "reshape", "permute_7": "permute", "unbind_7": "unbind", "Identity_116": "Identity", "Identity_117": "Identity", "scaled_dot_product_attention_7": "scaled_dot_product_attention", "transpose_17": "transpose", "reshape_27": "reshape", "Linear_26": "Linear", "Dropout_19": "Dropout", "Identity_118": "Identity", "Identity_119": "Identity", "__add___15": "__add__", "LayerNorm_16": "LayerNorm", "Linear_27": "Linear", "SiLU_29": "SiLU", "Dropout_20": "Dropout", "Identity_120": "Identity", "Linear_28": "Linear", "Dropout_21": "Dropout", "Identity_121": "Identity", "Identity_122": "Identity", "__add___16": "__add__", "LayerNorm_17": "LayerNorm", "Linear_29": "Linear", "reshape_28": "reshape", "permute_8": "permute", "unbind_8": "unbind", "Identity_123": "Identity", "Identity_124": "Identity", "scaled_dot_product_attention_8": "scaled_dot_product_attention", "transpose_18": "transpose", "reshape_29": "reshape", "Linear_30": "Linear", "Dropout_22": "Dropout", "Identity_125": "Identity", "Identity_126": "Identity", "__add___17": "__add__", "LayerNorm_18": "LayerNorm", "Linear_31": "Linear", "SiLU_30": "SiLU", "Dropout_23": "Dropout", "Identity_127": "Identity", "Linear_32": "Linear", "Dropout_24": "Dropout", "Identity_128": "Identity", "Identity_129": "Identity", "__add___18": "__add__", "LayerNorm_19": "LayerNorm", "Linear_33": "Linear", "reshape_30": "reshape", "permute_9": "permute", "unbind_9": "unbind", "Identity_130": "Identity", "Identity_131": "Identity", "scaled_dot_product_attention_9": "scaled_dot_product_attention", "transpose_19": "transpose", "reshape_31": "reshape", "Linear_34": "Linear", "Dropout_25": "Dropout", "Identity_132": "Identity", "Identity_133": "Identity", "__add___19": "__add__", "LayerNorm_20": "LayerNorm", "Linear_35": "Linear", "SiLU_31": "SiLU", "Dropout_26": "Dropout", "Identity_134": "Identity", "Linear_36": "Linear", "Dropout_27": "Dropout", "Identity_135": "Identity", "Identity_136": "Identity", "__add___20": "__add__", "LayerNorm_21": "LayerNorm", "contiguous_3": "contiguous", "view_3": "view", "transpose_20": "transpose", "reshape_32": "reshape", "transpose_21": "transpose", "reshape_33": "reshape", "Conv2d_33": "Conv2d", "add__30": "add_", "batch_norm_30": "batch_norm", "Identity_137": "Identity", "SiLU_32": "SiLU", "cat_3": "cat", "Conv2d_34": "Conv2d", "add__31": "add_", "batch_norm_31": "batch_norm", "Identity_138": "Identity", "SiLU_33": "SiLU", "Conv2d_35": "Conv2d", "add__32": "add_", "batch_norm_32": "batch_norm", "Identity_139": "Identity", "SiLU_34": "SiLU", "AdaptiveAvgPool2d_1": "AdaptiveAvgPool2d", "Flatten_1": "Flatten", "Dropout_28": "Dropout", "Linear_37": "Linear", "Identity_140": "Identity", "output": "output"};
    const ancestor_map = {"Conv2d_1": "ConvNormAct_1", "tensor_1": "BatchNormAct2d_1", "BatchNormAct2d_1": "ConvNormAct_1", "tensor_2": "BatchNormAct2d_1", "tensor_3": "BatchNormAct2d_1", "tensor_4": "BatchNormAct2d_1", "scalar_1": "BatchNormAct2d_1", "scalar_2": "BatchNormAct2d_1", "scalar_3": "BatchNormAct2d_1", "batch_norm_1": "BatchNormAct2d_1", "Identity_1": "BatchNormAct2d_1", "SiLU_1": "BatchNormAct2d_1", "Conv2d_2": "ConvNormAct_2", "ConvNormAct_2": "BottleneckBlock_1", "BottleneckBlock_1": "Sequential_2", "Sequential_2": "Sequential_1", "tensor_6": "BatchNormAct2d_2", "BatchNormAct2d_2": "ConvNormAct_2", "tensor_7": "BatchNormAct2d_2", "tensor_8": "BatchNormAct2d_2", "tensor_9": "BatchNormAct2d_2", "scalar_5": "BatchNormAct2d_2", "scalar_6": "BatchNormAct2d_2", "scalar_7": "BatchNormAct2d_2", "batch_norm_2": "BatchNormAct2d_2", "Identity_2": "BatchNormAct2d_2", "SiLU_2": "BatchNormAct2d_2", "Conv2d_3": "ConvNormAct_3", "ConvNormAct_3": "BottleneckBlock_1", "tensor_11": "BatchNormAct2d_3", "BatchNormAct2d_3": "ConvNormAct_3", "tensor_12": "BatchNormAct2d_3", "tensor_13": "BatchNormAct2d_3", "tensor_14": "BatchNormAct2d_3", "scalar_9": "BatchNormAct2d_3", "scalar_10": "BatchNormAct2d_3", "scalar_11": "BatchNormAct2d_3", "batch_norm_3": "BatchNormAct2d_3", "Identity_3": "BatchNormAct2d_3", "SiLU_3": "BatchNormAct2d_3", "Identity_4": "BottleneckBlock_1", "Identity_5": "BottleneckBlock_1", "Conv2d_4": "ConvNormAct_4", "ConvNormAct_4": "BottleneckBlock_1", "tensor_16": "BatchNormAct2d_4", "BatchNormAct2d_4": "ConvNormAct_4", "tensor_17": "BatchNormAct2d_4", "tensor_18": "BatchNormAct2d_4", "tensor_19": "BatchNormAct2d_4", "scalar_13": "BatchNormAct2d_4", "scalar_14": "BatchNormAct2d_4", "scalar_15": "BatchNormAct2d_4", "batch_norm_4": "BatchNormAct2d_4", "Identity_6": "BatchNormAct2d_4", "Identity_7": "BatchNormAct2d_4", "Identity_8": "BottleneckBlock_1", "Identity_9": "BottleneckBlock_1", "Identity_10": "BottleneckBlock_1", "Conv2d_5": "ConvNormAct_5", "ConvNormAct_5": "BottleneckBlock_2", "BottleneckBlock_2": "Sequential_3", "Sequential_3": "Sequential_1", "tensor_21": "BatchNormAct2d_5", "BatchNormAct2d_5": "ConvNormAct_5", "tensor_22": "BatchNormAct2d_5", "tensor_23": "BatchNormAct2d_5", "tensor_24": "BatchNormAct2d_5", "scalar_17": "BatchNormAct2d_5", "scalar_18": "BatchNormAct2d_5", "scalar_19": "BatchNormAct2d_5", "batch_norm_5": "BatchNormAct2d_5", "Identity_11": "BatchNormAct2d_5", "SiLU_4": "BatchNormAct2d_5", "Conv2d_6": "ConvNormAct_6", "ConvNormAct_6": "BottleneckBlock_2", "tensor_26": "BatchNormAct2d_6", "BatchNormAct2d_6": "ConvNormAct_6", "tensor_27": "BatchNormAct2d_6", "tensor_28": "BatchNormAct2d_6", "tensor_29": "BatchNormAct2d_6", "scalar_21": "BatchNormAct2d_6", "scalar_22": "BatchNormAct2d_6", "scalar_23": "BatchNormAct2d_6", "batch_norm_6": "BatchNormAct2d_6", "Identity_12": "BatchNormAct2d_6", "SiLU_5": "BatchNormAct2d_6", "Identity_13": "BottleneckBlock_2", "Identity_14": "BottleneckBlock_2", "Conv2d_7": "ConvNormAct_7", "ConvNormAct_7": "BottleneckBlock_2", "tensor_31": "BatchNormAct2d_7", "BatchNormAct2d_7": "ConvNormAct_7", "tensor_32": "BatchNormAct2d_7", "tensor_33": "BatchNormAct2d_7", "tensor_34": "BatchNormAct2d_7", "scalar_25": "BatchNormAct2d_7", "scalar_26": "BatchNormAct2d_7", "scalar_27": "BatchNormAct2d_7", "batch_norm_7": "BatchNormAct2d_7", "Identity_15": "BatchNormAct2d_7", "Identity_16": "BatchNormAct2d_7", "Identity_17": "BottleneckBlock_2", "Identity_18": "BottleneckBlock_2", "Identity_19": "BottleneckBlock_2", "Conv2d_8": "ConvNormAct_8", "ConvNormAct_8": "BottleneckBlock_3", "BottleneckBlock_3": "Sequential_3", "tensor_36": "BatchNormAct2d_8", "BatchNormAct2d_8": "ConvNormAct_8", "tensor_37": "BatchNormAct2d_8", "tensor_38": "BatchNormAct2d_8", "tensor_39": "BatchNormAct2d_8", "scalar_29": "BatchNormAct2d_8", "scalar_30": "BatchNormAct2d_8", "scalar_31": "BatchNormAct2d_8", "batch_norm_8": "BatchNormAct2d_8", "Identity_20": "BatchNormAct2d_8", "SiLU_6": "BatchNormAct2d_8", "Conv2d_9": "ConvNormAct_9", "ConvNormAct_9": "BottleneckBlock_3", "tensor_41": "BatchNormAct2d_9", "BatchNormAct2d_9": "ConvNormAct_9", "tensor_42": "BatchNormAct2d_9", "tensor_43": "BatchNormAct2d_9", "tensor_44": "BatchNormAct2d_9", "scalar_33": "BatchNormAct2d_9", "scalar_34": "BatchNormAct2d_9", "scalar_35": "BatchNormAct2d_9", "batch_norm_9": "BatchNormAct2d_9", "Identity_21": "BatchNormAct2d_9", "SiLU_7": "BatchNormAct2d_9", "Identity_22": "BottleneckBlock_3", "Identity_23": "BottleneckBlock_3", "Conv2d_10": "ConvNormAct_10", "ConvNormAct_10": "BottleneckBlock_3", "tensor_46": "BatchNormAct2d_10", "BatchNormAct2d_10": "ConvNormAct_10", "tensor_47": "BatchNormAct2d_10", "tensor_48": "BatchNormAct2d_10", "tensor_49": "BatchNormAct2d_10", "scalar_37": "BatchNormAct2d_10", "scalar_38": "BatchNormAct2d_10", "scalar_39": "BatchNormAct2d_10", "batch_norm_10": "BatchNormAct2d_10", "Identity_24": "BatchNormAct2d_10", "Identity_25": "BatchNormAct2d_10", "Identity_26": "BottleneckBlock_3", "Identity_27": "BottleneckBlock_3", "Identity_28": "BottleneckBlock_3", "__add___1": "BottleneckBlock_3", "Identity_29": "BottleneckBlock_3", "Conv2d_11": "ConvNormAct_11", "ConvNormAct_11": "BottleneckBlock_4", "BottleneckBlock_4": "Sequential_3", "tensor_51": "BatchNormAct2d_11", "BatchNormAct2d_11": "ConvNormAct_11", "tensor_52": "BatchNormAct2d_11", "tensor_53": "BatchNormAct2d_11", "tensor_54": "BatchNormAct2d_11", "scalar_41": "BatchNormAct2d_11", "scalar_42": "BatchNormAct2d_11", "scalar_43": "BatchNormAct2d_11", "batch_norm_11": "BatchNormAct2d_11", "Identity_30": "BatchNormAct2d_11", "SiLU_8": "BatchNormAct2d_11", "Conv2d_12": "ConvNormAct_12", "ConvNormAct_12": "BottleneckBlock_4", "tensor_56": "BatchNormAct2d_12", "BatchNormAct2d_12": "ConvNormAct_12", "tensor_57": "BatchNormAct2d_12", "tensor_58": "BatchNormAct2d_12", "tensor_59": "BatchNormAct2d_12", "scalar_45": "BatchNormAct2d_12", "scalar_46": "BatchNormAct2d_12", "scalar_47": "BatchNormAct2d_12", "batch_norm_12": "BatchNormAct2d_12", "Identity_31": "BatchNormAct2d_12", "SiLU_9": "BatchNormAct2d_12", "Identity_32": "BottleneckBlock_4", "Identity_33": "BottleneckBlock_4", "Conv2d_13": "ConvNormAct_13", "ConvNormAct_13": "BottleneckBlock_4", "tensor_61": "BatchNormAct2d_13", "BatchNormAct2d_13": "ConvNormAct_13", "tensor_62": "BatchNormAct2d_13", "tensor_63": "BatchNormAct2d_13", "tensor_64": "BatchNormAct2d_13", "scalar_49": "BatchNormAct2d_13", "scalar_50": "BatchNormAct2d_13", "scalar_51": "BatchNormAct2d_13", "batch_norm_13": "BatchNormAct2d_13", "Identity_34": "BatchNormAct2d_13", "Identity_35": "BatchNormAct2d_13", "Identity_36": "BottleneckBlock_4", "Identity_37": "BottleneckBlock_4", "Identity_38": "BottleneckBlock_4", "__add___2": "BottleneckBlock_4", "Identity_39": "BottleneckBlock_4", "Conv2d_14": "ConvNormAct_14", "ConvNormAct_14": "BottleneckBlock_5", "BottleneckBlock_5": "Sequential_4", "Sequential_4": "Sequential_1", "tensor_66": "BatchNormAct2d_14", "BatchNormAct2d_14": "ConvNormAct_14", "tensor_67": "BatchNormAct2d_14", "tensor_68": "BatchNormAct2d_14", "tensor_69": "BatchNormAct2d_14", "scalar_53": "BatchNormAct2d_14", "scalar_54": "BatchNormAct2d_14", "scalar_55": "BatchNormAct2d_14", "batch_norm_14": "BatchNormAct2d_14", "Identity_40": "BatchNormAct2d_14", "SiLU_10": "BatchNormAct2d_14", "Conv2d_15": "ConvNormAct_15", "ConvNormAct_15": "BottleneckBlock_5", "tensor_71": "BatchNormAct2d_15", "BatchNormAct2d_15": "ConvNormAct_15", "tensor_72": "BatchNormAct2d_15", "tensor_73": "BatchNormAct2d_15", "tensor_74": "BatchNormAct2d_15", "scalar_57": "BatchNormAct2d_15", "scalar_58": "BatchNormAct2d_15", "scalar_59": "BatchNormAct2d_15", "batch_norm_15": "BatchNormAct2d_15", "Identity_41": "BatchNormAct2d_15", "SiLU_11": "BatchNormAct2d_15", "Identity_42": "BottleneckBlock_5", "Identity_43": "BottleneckBlock_5", "Conv2d_16": "ConvNormAct_16", "ConvNormAct_16": "BottleneckBlock_5", "tensor_76": "BatchNormAct2d_16", "BatchNormAct2d_16": "ConvNormAct_16", "tensor_77": "BatchNormAct2d_16", "tensor_78": "BatchNormAct2d_16", "tensor_79": "BatchNormAct2d_16", "scalar_61": "BatchNormAct2d_16", "scalar_62": "BatchNormAct2d_16", "scalar_63": "BatchNormAct2d_16", "batch_norm_16": "BatchNormAct2d_16", "Identity_44": "BatchNormAct2d_16", "Identity_45": "BatchNormAct2d_16", "Identity_46": "BottleneckBlock_5", "Identity_47": "BottleneckBlock_5", "Identity_48": "BottleneckBlock_5", "Conv2d_17": "ConvNormAct_17", "ConvNormAct_17": "MobileVitBlock_1", "MobileVitBlock_1": "Sequential_4", "tensor_81": "BatchNormAct2d_17", "BatchNormAct2d_17": "ConvNormAct_17", "tensor_82": "BatchNormAct2d_17", "tensor_83": "BatchNormAct2d_17", "tensor_84": "BatchNormAct2d_17", "scalar_65": "BatchNormAct2d_17", "scalar_66": "BatchNormAct2d_17", "scalar_67": "BatchNormAct2d_17", "batch_norm_17": "BatchNormAct2d_17", "Identity_49": "BatchNormAct2d_17", "SiLU_12": "BatchNormAct2d_17", "Conv2d_18": "MobileVitBlock_1", "scalar_68": "MobileVitBlock_1", "scalar_69": "MobileVitBlock_1", "scalar_70": "MobileVitBlock_1", "scalar_71": "MobileVitBlock_1", "reshape_1": "MobileVitBlock_1", "scalar_72": "MobileVitBlock_1", "scalar_73": "MobileVitBlock_1", "transpose_1": "MobileVitBlock_1", "scalar_74": "MobileVitBlock_1", "scalar_75": "MobileVitBlock_1", "scalar_76": "MobileVitBlock_1", "scalar_77": "MobileVitBlock_1", "reshape_2": "MobileVitBlock_1", "scalar_78": "MobileVitBlock_1", "scalar_79": "MobileVitBlock_1", "transpose_2": "MobileVitBlock_1", "scalar_80": "MobileVitBlock_1", "scalar_81": "MobileVitBlock_1", "scalar_82": "MobileVitBlock_1", "reshape_3": "MobileVitBlock_1", "LayerNorm_1": "Block_1", "Block_1": "Sequential_5", "Sequential_5": "MobileVitBlock_1", "Linear_1": "Attention_1", "Attention_1": "Block_1", "scalar_83": "Attention_1", "scalar_84": "Attention_1", "scalar_85": "Attention_1", "scalar_86": "Attention_1", "scalar_87": "Attention_1", "reshape_4": "Attention_1", "scalar_88": "Attention_1", "scalar_89": "Attention_1", "scalar_90": "Attention_1", "scalar_91": "Attention_1", "scalar_92": "Attention_1", "permute_1": "Attention_1", "scalar_93": "Attention_1", "unbind_1": "Attention_1", "Identity_50": "Attention_1", "Identity_51": "Attention_1", "scaled_dot_product_attention_1": "Attention_1", "scalar_94": "Attention_1", "scalar_95": "Attention_1", "transpose_3": "Attention_1", "scalar_96": "Attention_1", "scalar_97": "Attention_1", "scalar_98": "Attention_1", "reshape_5": "Attention_1", "Linear_2": "Attention_1", "Dropout_1": "Attention_1", "Identity_52": "Block_1", "Identity_53": "Block_1", "__add___3": "Block_1", "LayerNorm_2": "Block_1", "Linear_3": "Mlp_1", "Mlp_1": "Block_1", "SiLU_13": "Mlp_1", "Dropout_2": "Mlp_1", "Identity_54": "Mlp_1", "Linear_4": "Mlp_1", "Dropout_3": "Mlp_1", "Identity_55": "Block_1", "Identity_56": "Block_1", "__add___4": "Block_1", "LayerNorm_3": "Block_2", "Block_2": "Sequential_5", "Linear_5": "Attention_2", "Attention_2": "Block_2", "scalar_99": "Attention_2", "scalar_100": "Attention_2", "scalar_101": "Attention_2", "scalar_102": "Attention_2", "scalar_103": "Attention_2", "reshape_6": "Attention_2", "scalar_104": "Attention_2", "scalar_105": "Attention_2", "scalar_106": "Attention_2", "scalar_107": "Attention_2", "scalar_108": "Attention_2", "permute_2": "Attention_2", "scalar_109": "Attention_2", "unbind_2": "Attention_2", "Identity_57": "Attention_2", "Identity_58": "Attention_2", "scaled_dot_product_attention_2": "Attention_2", "scalar_110": "Attention_2", "scalar_111": "Attention_2", "transpose_4": "Attention_2", "scalar_112": "Attention_2", "scalar_113": "Attention_2", "scalar_114": "Attention_2", "reshape_7": "Attention_2", "Linear_6": "Attention_2", "Dropout_4": "Attention_2", "Identity_59": "Block_2", "Identity_60": "Block_2", "__add___5": "Block_2", "LayerNorm_4": "Block_2", "Linear_7": "Mlp_2", "Mlp_2": "Block_2", "SiLU_14": "Mlp_2", "Dropout_5": "Mlp_2", "Identity_61": "Mlp_2", "Linear_8": "Mlp_2", "Dropout_6": "Mlp_2", "Identity_62": "Block_2", "Identity_63": "Block_2", "__add___6": "Block_2", "LayerNorm_5": "MobileVitBlock_1", "contiguous_1": "MobileVitBlock_1", "scalar_115": "MobileVitBlock_1", "scalar_116": "MobileVitBlock_1", "scalar_117": "MobileVitBlock_1", "scalar_118": "MobileVitBlock_1", "view_1": "MobileVitBlock_1", "scalar_119": "MobileVitBlock_1", "scalar_120": "MobileVitBlock_1", "transpose_5": "MobileVitBlock_1", "scalar_121": "MobileVitBlock_1", "scalar_122": "MobileVitBlock_1", "scalar_123": "MobileVitBlock_1", "scalar_124": "MobileVitBlock_1", "reshape_8": "MobileVitBlock_1", "scalar_125": "MobileVitBlock_1", "scalar_126": "MobileVitBlock_1", "transpose_6": "MobileVitBlock_1", "scalar_127": "MobileVitBlock_1", "scalar_128": "MobileVitBlock_1", "scalar_129": "MobileVitBlock_1", "scalar_130": "MobileVitBlock_1", "reshape_9": "MobileVitBlock_1", "Conv2d_19": "ConvNormAct_18", "ConvNormAct_18": "MobileVitBlock_1", "tensor_86": "BatchNormAct2d_18", "BatchNormAct2d_18": "ConvNormAct_18", "tensor_87": "BatchNormAct2d_18", "tensor_88": "BatchNormAct2d_18", "tensor_89": "BatchNormAct2d_18", "scalar_132": "BatchNormAct2d_18", "scalar_133": "BatchNormAct2d_18", "scalar_134": "BatchNormAct2d_18", "batch_norm_18": "BatchNormAct2d_18", "Identity_64": "BatchNormAct2d_18", "SiLU_15": "BatchNormAct2d_18", "cat_1": "MobileVitBlock_1", "Conv2d_20": "ConvNormAct_19", "ConvNormAct_19": "MobileVitBlock_1", "tensor_91": "BatchNormAct2d_19", "BatchNormAct2d_19": "ConvNormAct_19", "tensor_92": "BatchNormAct2d_19", "tensor_93": "BatchNormAct2d_19", "tensor_94": "BatchNormAct2d_19", "scalar_136": "BatchNormAct2d_19", "scalar_137": "BatchNormAct2d_19", "scalar_138": "BatchNormAct2d_19", "batch_norm_19": "BatchNormAct2d_19", "Identity_65": "BatchNormAct2d_19", "SiLU_16": "BatchNormAct2d_19", "Conv2d_21": "ConvNormAct_20", "ConvNormAct_20": "BottleneckBlock_6", "BottleneckBlock_6": "Sequential_6", "Sequential_6": "Sequential_1", "tensor_96": "BatchNormAct2d_20", "BatchNormAct2d_20": "ConvNormAct_20", "tensor_97": "BatchNormAct2d_20", "tensor_98": "BatchNormAct2d_20", "tensor_99": "BatchNormAct2d_20", "scalar_140": "BatchNormAct2d_20", "scalar_141": "BatchNormAct2d_20", "scalar_142": "BatchNormAct2d_20", "batch_norm_20": "BatchNormAct2d_20", "Identity_66": "BatchNormAct2d_20", "SiLU_17": "BatchNormAct2d_20", "Conv2d_22": "ConvNormAct_21", "ConvNormAct_21": "BottleneckBlock_6", "tensor_101": "BatchNormAct2d_21", "BatchNormAct2d_21": "ConvNormAct_21", "tensor_102": "BatchNormAct2d_21", "tensor_103": "BatchNormAct2d_21", "tensor_104": "BatchNormAct2d_21", "scalar_144": "BatchNormAct2d_21", "scalar_145": "BatchNormAct2d_21", "scalar_146": "BatchNormAct2d_21", "batch_norm_21": "BatchNormAct2d_21", "Identity_67": "BatchNormAct2d_21", "SiLU_18": "BatchNormAct2d_21", "Identity_68": "BottleneckBlock_6", "Identity_69": "BottleneckBlock_6", "Conv2d_23": "ConvNormAct_22", "ConvNormAct_22": "BottleneckBlock_6", "tensor_106": "BatchNormAct2d_22", "BatchNormAct2d_22": 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"Attention_4": "Block_4", "scalar_186": "Attention_4", "scalar_187": "Attention_4", "scalar_188": "Attention_4", "scalar_189": "Attention_4", "scalar_190": "Attention_4", "reshape_15": "Attention_4", "scalar_191": "Attention_4", "scalar_192": "Attention_4", "scalar_193": "Attention_4", "scalar_194": "Attention_4", "scalar_195": "Attention_4", "permute_4": "Attention_4", "scalar_196": "Attention_4", "unbind_4": "Attention_4", "Identity_83": "Attention_4", "Identity_84": "Attention_4", "scaled_dot_product_attention_4": "Attention_4", "scalar_197": "Attention_4", "scalar_198": "Attention_4", "transpose_10": "Attention_4", "scalar_199": "Attention_4", "scalar_200": "Attention_4", "scalar_201": "Attention_4", "reshape_16": "Attention_4", "Linear_14": "Attention_4", "Dropout_10": "Attention_4", "Identity_85": "Block_4", "Identity_86": "Block_4", "__add___9": "Block_4", "LayerNorm_9": "Block_4", "Linear_15": "Mlp_4", "Mlp_4": "Block_4", "SiLU_21": "Mlp_4", "Dropout_11": "Mlp_4", "Identity_87": "Mlp_4", "Linear_16": "Mlp_4", "Dropout_12": "Mlp_4", "Identity_88": "Block_4", "Identity_89": "Block_4", "__add___10": "Block_4", "LayerNorm_10": "Block_5", "Block_5": "Sequential_7", "Linear_17": "Attention_5", "Attention_5": "Block_5", "scalar_202": "Attention_5", "scalar_203": "Attention_5", "scalar_204": "Attention_5", "scalar_205": "Attention_5", "scalar_206": "Attention_5", "reshape_17": "Attention_5", "scalar_207": "Attention_5", "scalar_208": "Attention_5", "scalar_209": "Attention_5", "scalar_210": "Attention_5", "scalar_211": "Attention_5", "permute_5": "Attention_5", "scalar_212": "Attention_5", "unbind_5": "Attention_5", "Identity_90": "Attention_5", "Identity_91": "Attention_5", "scaled_dot_product_attention_5": "Attention_5", "scalar_213": "Attention_5", "scalar_214": "Attention_5", "transpose_11": "Attention_5", "scalar_215": "Attention_5", "scalar_216": "Attention_5", "scalar_217": "Attention_5", "reshape_18": "Attention_5", "Linear_18": "Attention_5", "Dropout_13": "Attention_5", "Identity_92": "Block_5", "Identity_93": "Block_5", "__add___11": "Block_5", "LayerNorm_11": "Block_5", "Linear_19": "Mlp_5", "Mlp_5": "Block_5", "SiLU_22": "Mlp_5", "Dropout_14": "Mlp_5", "Identity_94": "Mlp_5", "Linear_20": "Mlp_5", "Dropout_15": "Mlp_5", "Identity_95": "Block_5", "Identity_96": "Block_5", "__add___12": "Block_5", "LayerNorm_12": "Block_6", "Block_6": "Sequential_7", "Linear_21": "Attention_6", "Attention_6": "Block_6", "scalar_218": "Attention_6", "scalar_219": "Attention_6", "scalar_220": "Attention_6", "scalar_221": "Attention_6", "scalar_222": "Attention_6", "reshape_19": "Attention_6", "scalar_223": "Attention_6", "scalar_224": "Attention_6", "scalar_225": "Attention_6", "scalar_226": "Attention_6", "scalar_227": "Attention_6", "permute_6": "Attention_6", "scalar_228": "Attention_6", "unbind_6": "Attention_6", "Identity_97": "Attention_6", "Identity_98": "Attention_6", "scaled_dot_product_attention_6": "Attention_6", "scalar_229": "Attention_6", "scalar_230": "Attention_6", "transpose_12": "Attention_6", "scalar_231": "Attention_6", "scalar_232": "Attention_6", "scalar_233": "Attention_6", "reshape_20": "Attention_6", "Linear_22": "Attention_6", "Dropout_16": "Attention_6", "Identity_99": "Block_6", "Identity_100": "Block_6", "__add___13": "Block_6", "LayerNorm_13": "Block_6", "Linear_23": "Mlp_6", "Mlp_6": "Block_6", "SiLU_23": "Mlp_6", "Dropout_17": "Mlp_6", "Identity_101": "Mlp_6", "Linear_24": "Mlp_6", "Dropout_18": "Mlp_6", "Identity_102": "Block_6", "Identity_103": "Block_6", "__add___14": "Block_6", "LayerNorm_14": "MobileVitBlock_2", "contiguous_2": "MobileVitBlock_2", "scalar_234": "MobileVitBlock_2", "scalar_235": "MobileVitBlock_2", "scalar_236": "MobileVitBlock_2", "scalar_237": "MobileVitBlock_2", "view_2": "MobileVitBlock_2", "scalar_238": "MobileVitBlock_2", "scalar_239": "MobileVitBlock_2", "transpose_13": "MobileVitBlock_2", "scalar_240": "MobileVitBlock_2", "scalar_241": "MobileVitBlock_2", "scalar_242": "MobileVitBlock_2", "scalar_243": "MobileVitBlock_2", "reshape_21": "MobileVitBlock_2", "scalar_244": "MobileVitBlock_2", "scalar_245": "MobileVitBlock_2", "transpose_14": "MobileVitBlock_2", "scalar_246": "MobileVitBlock_2", "scalar_247": "MobileVitBlock_2", "scalar_248": "MobileVitBlock_2", "scalar_249": "MobileVitBlock_2", "reshape_22": "MobileVitBlock_2", "Conv2d_26": "ConvNormAct_24", "ConvNormAct_24": "MobileVitBlock_2", "tensor_116": "BatchNormAct2d_24", "BatchNormAct2d_24": "ConvNormAct_24", "tensor_117": "BatchNormAct2d_24", "tensor_118": "BatchNormAct2d_24", "tensor_119": "BatchNormAct2d_24", "scalar_251": "BatchNormAct2d_24", "scalar_252": "BatchNormAct2d_24", "scalar_253": "BatchNormAct2d_24", "batch_norm_24": "BatchNormAct2d_24", "Identity_104": "BatchNormAct2d_24", "SiLU_24": "BatchNormAct2d_24", "cat_2": "MobileVitBlock_2", "Conv2d_27": "ConvNormAct_25", "ConvNormAct_25": "MobileVitBlock_2", "tensor_121": "BatchNormAct2d_25", "BatchNormAct2d_25": "ConvNormAct_25", "tensor_122": "BatchNormAct2d_25", "tensor_123": "BatchNormAct2d_25", "tensor_124": "BatchNormAct2d_25", "scalar_255": "BatchNormAct2d_25", "scalar_256": "BatchNormAct2d_25", "scalar_257": "BatchNormAct2d_25", "batch_norm_25": "BatchNormAct2d_25", "Identity_105": "BatchNormAct2d_25", "SiLU_25": "BatchNormAct2d_25", "Conv2d_28": "ConvNormAct_26", "ConvNormAct_26": "BottleneckBlock_7", "BottleneckBlock_7": "Sequential_8", "Sequential_8": "Sequential_1", "tensor_126": "BatchNormAct2d_26", "BatchNormAct2d_26": "ConvNormAct_26", "tensor_127": "BatchNormAct2d_26", "tensor_128": "BatchNormAct2d_26", "tensor_129": "BatchNormAct2d_26", "scalar_259": "BatchNormAct2d_26", "scalar_260": "BatchNormAct2d_26", "scalar_261": "BatchNormAct2d_26", "batch_norm_26": "BatchNormAct2d_26", "Identity_106": "BatchNormAct2d_26", "SiLU_26": "BatchNormAct2d_26", "Conv2d_29": "ConvNormAct_27", "ConvNormAct_27": "BottleneckBlock_7", "tensor_131": "BatchNormAct2d_27", "BatchNormAct2d_27": 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"scalar_285": "MobileVitBlock_3", "transpose_16": "MobileVitBlock_3", "scalar_286": "MobileVitBlock_3", "scalar_287": "MobileVitBlock_3", "scalar_288": "MobileVitBlock_3", "reshape_25": "MobileVitBlock_3", "LayerNorm_15": "Block_7", "Block_7": "Sequential_9", "Sequential_9": "MobileVitBlock_3", "Linear_25": "Attention_7", "Attention_7": "Block_7", "scalar_289": "Attention_7", "scalar_290": "Attention_7", "scalar_291": "Attention_7", "scalar_292": "Attention_7", "scalar_293": "Attention_7", "reshape_26": "Attention_7", "scalar_294": "Attention_7", "scalar_295": "Attention_7", "scalar_296": "Attention_7", "scalar_297": "Attention_7", "scalar_298": "Attention_7", "permute_7": "Attention_7", "scalar_299": "Attention_7", "unbind_7": "Attention_7", "Identity_116": "Attention_7", "Identity_117": "Attention_7", "scaled_dot_product_attention_7": "Attention_7", "scalar_300": "Attention_7", "scalar_301": "Attention_7", "transpose_17": "Attention_7", "scalar_302": "Attention_7", "scalar_303": 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"Attention_8", "Identity_124": "Attention_8", "scaled_dot_product_attention_8": "Attention_8", "scalar_316": "Attention_8", "scalar_317": "Attention_8", "transpose_18": "Attention_8", "scalar_318": "Attention_8", "scalar_319": "Attention_8", "scalar_320": "Attention_8", "reshape_29": "Attention_8", "Linear_30": "Attention_8", "Dropout_22": "Attention_8", "Identity_125": "Block_8", "Identity_126": "Block_8", "__add___17": "Block_8", "LayerNorm_18": "Block_8", "Linear_31": "Mlp_8", "Mlp_8": "Block_8", "SiLU_30": "Mlp_8", "Dropout_23": "Mlp_8", "Identity_127": "Mlp_8", "Linear_32": "Mlp_8", "Dropout_24": "Mlp_8", "Identity_128": "Block_8", "Identity_129": "Block_8", "__add___18": "Block_8", "LayerNorm_19": "Block_9", "Block_9": "Sequential_9", "Linear_33": "Attention_9", "Attention_9": "Block_9", "scalar_321": "Attention_9", "scalar_322": "Attention_9", "scalar_323": "Attention_9", "scalar_324": "Attention_9", "scalar_325": "Attention_9", "reshape_30": "Attention_9", "scalar_326": 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"scalar_337": "MobileVitBlock_3", "scalar_338": "MobileVitBlock_3", "scalar_339": "MobileVitBlock_3", "scalar_340": "MobileVitBlock_3", "view_3": "MobileVitBlock_3", "scalar_341": "MobileVitBlock_3", "scalar_342": "MobileVitBlock_3", "transpose_20": "MobileVitBlock_3", "scalar_343": "MobileVitBlock_3", "scalar_344": "MobileVitBlock_3", "scalar_345": "MobileVitBlock_3", "scalar_346": "MobileVitBlock_3", "reshape_32": "MobileVitBlock_3", "scalar_347": "MobileVitBlock_3", "scalar_348": "MobileVitBlock_3", "transpose_21": "MobileVitBlock_3", "scalar_349": "MobileVitBlock_3", "scalar_350": "MobileVitBlock_3", "scalar_351": "MobileVitBlock_3", "scalar_352": "MobileVitBlock_3", "reshape_33": "MobileVitBlock_3", "Conv2d_33": "ConvNormAct_30", "ConvNormAct_30": "MobileVitBlock_3", "tensor_146": "BatchNormAct2d_30", "BatchNormAct2d_30": "ConvNormAct_30", "tensor_147": "BatchNormAct2d_30", "tensor_148": "BatchNormAct2d_30", "tensor_149": "BatchNormAct2d_30", "scalar_354": "BatchNormAct2d_30", "scalar_355": "BatchNormAct2d_30", "scalar_356": "BatchNormAct2d_30", "batch_norm_30": "BatchNormAct2d_30", "Identity_137": "BatchNormAct2d_30", "SiLU_32": "BatchNormAct2d_30", "cat_3": "MobileVitBlock_3", "Conv2d_34": "ConvNormAct_31", "ConvNormAct_31": "MobileVitBlock_3", "tensor_151": "BatchNormAct2d_31", "BatchNormAct2d_31": "ConvNormAct_31", "tensor_152": "BatchNormAct2d_31", "tensor_153": "BatchNormAct2d_31", "tensor_154": "BatchNormAct2d_31", "scalar_358": "BatchNormAct2d_31", "scalar_359": "BatchNormAct2d_31", "scalar_360": "BatchNormAct2d_31", "batch_norm_31": "BatchNormAct2d_31", "Identity_138": "BatchNormAct2d_31", "SiLU_33": "BatchNormAct2d_31", "Conv2d_35": "ConvNormAct_32", "tensor_156": "BatchNormAct2d_32", "BatchNormAct2d_32": "ConvNormAct_32", "tensor_157": "BatchNormAct2d_32", "tensor_158": "BatchNormAct2d_32", "tensor_159": "BatchNormAct2d_32", "scalar_362": "BatchNormAct2d_32", "scalar_363": "BatchNormAct2d_32", "scalar_364": "BatchNormAct2d_32", "batch_norm_32": 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    var collapsedContainers = new Set();
    const containerNodes = new Set();
    var adj_list_collapsed_nodes = {};
    let vizInstance = null;

    const EXPAND_TARGET_COVERAGE = 0.8;
    const COLLAPSE_TARGET_COVERAGE = 0.2;
    let lastModifiedContainer = null; // Track the last modified container
    let lastOperation = null; // Track whether it was 'expand' or 'collapse'
    
    function waitForLibs(callback) {
        if (typeof d3 !== 'undefined' && typeof Viz !== 'undefined') {
            Viz.instance().then(viz => {
                vizInstance = viz;
                callback();
            });
        } else {
            setTimeout(function() { waitForLibs(callback) }, 400);
        }
    }

    function extractGraphData(graphData) {
        const result = {
            nodes: {},
            edges: [],
            clusters: {}
        };

        // Process nodes - they have _gvid property and may have a "pos" attribute
        for (const obj of graphData.objects) {
            // Skip cluster objects (which are subgraphs)
            if (obj.name && obj.name.startsWith("cluster_")) {
                result.clusters[obj.name.replace("cluster_", "")] = {
                    bb: obj.bb, // Bounding box "x1,y1,x2,y2"
                    label: obj.label,
                    nodes: obj.nodes ? obj.nodes.map(id => graphData.objects[id].name) : []
                };
                continue;
            }
        
            // This is a node
            if (obj.pos) {
            const [x, y] = obj.pos.split(",").map(Number);
                result.nodes[obj.name] = {
                id: obj._gvid,
                name: obj.name,
                position: { x, y },
                    width: parseFloat(obj.width) * 72, // Convert to pixels (72 points per inch)
                    height: parseFloat(obj.height) * 72,
                    label: obj.label === "\\N" ? obj.name : obj.label
                };
            }
        }

        // Process edges
        for (const edge of graphData.edges) {
            const sourceId = edge.tail;
            const targetId = edge.head;
            
            // Find node names by their _gvid
            const sourceName = Object.values(result.nodes).find(n => n.id === sourceId)?.name;
            const targetName = Object.values(result.nodes).find(n => n.id === targetId)?.name;
            
            if (!sourceName || !targetName) continue;
            
                // Extract path points from the pos attribute
            let pathPoints = [];
            if (edge.pos) {
                // IMPORTANT CHANGE: Correctly handle the "e," prefix
                const posStr = edge.pos;
                // Check if the string starts with "e,"
                if (posStr.startsWith("e,")) {
                    // Split the string and extract coordinates correctly
                    const parts = posStr.split(" ");
                    // Extract the endpoint (first coordinate after "e,")
                    const endPoint = parts[0].substring(2).split(",").map(Number);
                    // Add the remaining points
                    pathPoints = parts.slice(1).map(point => {
                        const [x, y] = point.split(',').map(Number);
                        return { x, y };
                    });
                    
                    // Add the endpoint as the last point in the path
                    pathPoints.push({ x: endPoint[0], y: endPoint[1] });
                } else {
                    // Handle normal path without "e," prefix
                    pathPoints = posStr.split(' ').map(point => {
                    const [x, y] = point.split(',').map(Number);
                    return { x, y };
                    });
                }
            }
        
            result.edges.push({
                source: sourceName,
                target: targetName,
                path: pathPoints
            });
        }

        return result;
    }

    function identifyContainersWithFailedNodes(adj_list, ancestor_map) {
        const failedNodes = Object.keys(adj_list).filter(node => adj_list[node].failed);
      
        const containersWithFailedNodes = new Set();
      
        failedNodes.forEach(failedNode => {
            let currentNode = failedNode;

            while (ancestor_map[currentNode]) {
                const container = ancestor_map[currentNode];
                containersWithFailedNodes.add(container);
                currentNode = container;
            }
        });
      
        return containersWithFailedNodes;
    }


    function generateDotSource(adj_list, ancestor_map) {
        let dotSource = 'digraph G {\n';
        dotSource += '  rankdir=BT;\n'; // Top to bottom layout
        dotSource += '  node [shape=box];\n';

        // Clear and rebuild the containerNodes set
        containerNodes.clear();
        adj_list_collapsed_nodes = {};
        for (const value of Object.values(ancestor_map)) {
            if (value !== null && value !== undefined) {
                containerNodes.add(value);
            }
        }

        // Build container hierarchy
        const containerHierarchy = {};
        for (const [node, parent] of Object.entries(ancestor_map)) {
            if (parent && !containerHierarchy[parent]) {
                containerHierarchy[parent] = [];
            }
            if (parent) {
                containerHierarchy[parent].push(node);
            }
        }

        // Find top containers
        const topContainers = new Set();
        for (const container of containerNodes) {
            // A top container is one that doesn't have an ancestor itself
            if (!ancestor_map[container]) {
                topContainers.add(container);
            }
        }

        // Helper function to get all descendants of a container
        function getAllDescendants(container) {
            let descendants = [];
            const children = containerHierarchy[container] || [];

            for (const child of children) {
                descendants.push(child);
                if (containerNodes.has(child)) {
                    descendants = descendants.concat(getAllDescendants(child));
                }
            }

            return descendants;
        }

        // Process containers
        function processContainer(containerName, level = 0) {
            const indent = ' '.repeat(level * 2);

            // Check if this container is collapsed
            if (collapsedContainers.has(containerName)) {
              // For collapsed containers, create a normal node instead of a subgraph
                dotSource += `${indent}"${containerName}";\n`;
                return;
            }

            // Start subgraph for non-collapsed containers
            dotSource += `${indent}subgraph cluster_${containerName} {\n`;
            dotSource += `${indent}  label="${containerName}";\n`;
            dotSource += `${indent}  style=rounded;\n`;

            // Get all direct children
            const children = containerHierarchy[containerName] || [];

            // Process child containers first
            for (const child of children) {
                if (containerNodes.has(child)) {
                    processContainer(child, level + 1);
                }
            }

            // Add regular nodes
            for (const child of children) {
                if (!containerNodes.has(child)) {
                    if (adj_list[child] && adj_list[child].node_type === "Constant") {
                        dotSource += `${indent}  "${child}" [width=0.7, height=0.3];\n`;
                    } else if (adj_list[child] && (adj_list[child].node_type === "Input" || adj_list[child].node_type === "Output")) {
                        dotSource += `${indent}  "${child}" [width=1.1, height=0.5];\n`;
                    } else {
                        dotSource += `${indent}  "${child}";\n`;
                    }
                }
            }

            // Close subgraph
            dotSource += `${indent}}\n`;
        }

        // Process all top containers
        for (const container of topContainers) {
            processContainer(container);
        }

        // Add standalone nodes (not in containers)
        for (const node of Object.keys(adj_list)) {
            if (!ancestor_map[node]) {
                if (adj_list[node] && adj_list[node].node_type === "Constant") {
                    dotSource += `  "${node}" [width=0.7, height=0.3];\n`;
                } else if (adj_list[node] && (adj_list[node].node_type === "Input" || adj_list[node].node_type === "Output")) {
                    dotSource += `  "${node}" [width=1.1, height=0.5];\n`;
                } else {
                    dotSource += `  "${node}";\n`;
                }
            }
        }

        // Process edges, handling collapsed containers
        for (const [node, data] of Object.entries(adj_list)) {
            for (const edge of data.edges) {
                let sourceNode = node;
                let targetNode = edge.target;
                let dims = edge.dims;
                let skipEdge = false;

                // Check if source node is inside a collapsed container
                for (const collapsedContainer of collapsedContainers) {
                    const descendants = getAllDescendants(collapsedContainer);

                    if (descendants.includes(node)) {
                        // Source node is inside a collapsed container, redirect edge from container
                        sourceNode = collapsedContainer;
                    }

                    if (descendants.includes(edge.target)) {
                        // Target node is inside a collapsed container, redirect edge to container
                        targetNode = collapsedContainer;
                    }

                    // Skip edges between nodes inside the same collapsed container
                    if (descendants.includes(node) && descendants.includes(edge.target)) {
                        skipEdge = true;
                    }
                }

                if (!skipEdge && sourceNode !== targetNode) {
                    dotSource += `  "${sourceNode}" -> "${targetNode}";\n`;
                      if (!(sourceNode in adj_list_collapsed_nodes)) {
                          adj_list_collapsed_nodes[sourceNode] = {'edges': []};
                      }
                      adj_list_collapsed_nodes[sourceNode]['edges'].push({'target': targetNode, 'dims': dims});
                }
            }
        }

        dotSource += '}';
        return dotSource;
    }

        
    function calculateBoundingBox(nodeGroup, nodePositions) {
        let minX = Infinity, minY = Infinity, maxX = -Infinity, maxY = -Infinity;
        nodeGroup.forEach(nodeName => {
            const node = nodePositions[nodeName];
            if (node) {
                minX = Math.min(minX, node.x - node.width / 2);
                minY = Math.min(minY, node.y - node.height / 2);
                maxX = Math.max(maxX, node.x + node.width / 2);
                maxY = Math.max(maxY, node.y + node.height / 2);
            }
        });
        return { x: minX, y: minY, width: maxX - minX, height: maxY - minY };
    }

    function reloadGraph() {
        d3.select("#graph_65f5e9a0-380e-4bcb-8217-2a9193d7ba29").html("");
        const dotSource = generateDotSource(adj_list, ancestor_map);

        graphData = vizInstance.renderJSON(dotSource)
        // Extract the graph data with positions from Graphviz
        const extractedGraphData = extractGraphData(graphData);

        
        // Now create the graph with the Graphviz layout data
        createGraph(extractedGraphData);

        // renderGraphvizImage(dotSource);
    }
                    
    waitForLibs(function() {                                
        reloadGraph();
    });

    function findErrorNode(nodes) {
        return nodes.find(node => adj_list[node].failed);
    }



    // Add this function to render the raw Graphviz visualization
    function renderGraphvizImage(dotSource) {
        // Create a container for the Graphviz image
        const graphvizContainer = d3.select("#graph-container_65f5e9a0-380e-4bcb-8217-2a9193d7ba29")
            .append("div")
            .attr("id", "graphviz-image-container")
            .style("margin-top", "20px")
            .style("border-top", "1px solid #ccc")
            .style("padding-top", "20px");
      
        graphvizContainer.append("h3")
            .text("Original Graphviz Rendering")
            .style("text-align", "center");
        
        // Use Viz.js to render the DOT source directly to SVG
        svgElement = vizInstance.renderSVGElement(dotSource)
        // Append the SVG element to the container
        graphvizContainer.node().appendChild(svgElement);
          
        // Style the SVG
        d3.select(svgElement)
            .attr("width", "100%")
            .attr("height", "600px")
            .style("background-color", "#f9f9f9");
        
    }

    function createGraph(graphvizData) {
        const svg = d3.select("#graph_65f5e9a0-380e-4bcb-8217-2a9193d7ba29")
        .append("svg")
        .attr("width", "100%")
        .attr("height", "100%")
        .style("background-color", "#f3f3eb");

        const inner = svg.append("g");

        // Add zoom behavior
        const zoom = d3.zoom().on("zoom", function(event) {
            inner.attr("transform", event.transform);
            closePopup();
        });

        svg.call(zoom);

        // Define arrowhead marker
        svg.append("defs").append("marker")
        .attr("id", "arrowhead_65f5e9a0-380e-4bcb-8217-2a9193d7ba29")
        .attr("viewBox", "0 -5 10 10")
        .attr("refX", 8)
        .attr("refY", 0)
        .attr("markerWidth", 6)
        .attr("markerHeight", 6)
        .attr("orient", "auto")
        .append("path")
        .attr("d", "M0,-5L10,0L0,5")
        .attr("fill", "#333");

        // Draw clusters (container boxes) first so they're behind nodes
        const clusters = inner.selectAll(".boundary-box")
        .data(Object.entries(graphvizData.clusters))
        .enter()
        .append("g")
        .attr("class", "boundary-box");

        const containersWithFailedNodes = identifyContainersWithFailedNodes(adj_list, ancestor_map);

        clusters.each(function([clusterName, clusterData]) {
            const group = d3.select(this);

            

            const hasFailedNodes = containersWithFailedNodes.has(clusterName);
            if (hasFailedNodes) {
                group.classed("error", true);
            }

            // Parse bounding box coordinates
            const [x1, y1, x2, y2] = clusterData.bb.split(",").map(Number);
            
            // Create boundary box
            group.append("rect")
            .attr("x", x1)
            .attr("y", y1)
            .attr("width", x2 - x1)
            .attr("height", y2 - y1)
            .attr("fill-opacity", 0.0)
            .attr("stroke", "black")
            .attr("stroke-width", 1)
            .attr("rx", 5)
            .attr("ry", 5);
                
            // Create label for the cluster
            const textGroup = group.append("g")
            .attr("transform", `translate(${x1 + 10}, ${y1 + 3})`);
            
            const text = textGroup.append("text")
            .text(clusterData.label)
            .attr("text-anchor", "start")
            .attr("dominant-baseline", "hanging")
            .attr("dy", "-6")
            .attr("font-size", 7)
            .attr("fill", "#333")
            .attr("stroke", "#f3f3eb")
            .attr("stroke-width", "5px")
            .attr("paint-order", "stroke")
            .attr("stroke-linejoin", "round");
                        
            
            // Add collapse/expand button
            group.append("circle")
            .attr("class", "collapse-button")
            .attr("cx", x1 + 10)
            .attr("cy", y1 + 10)
            .attr("r", 6)
            .attr("fill", "#fff")
            .attr("stroke", "#333")
            .attr("stroke-width", 1)
            .attr("cursor", "pointer");
            
            // Add "-" symbol
            group.append("text")
            .attr("class", "collapse-icon")
            .attr("x", x1 + 10)
            .attr("y", y1 + 10)
            .attr("text-anchor", "middle")
            .attr("dominant-baseline", "middle")
            .attr("font-size", "10px")
            .attr("font-weight", "bold")
            .attr("cursor", "pointer")
            .text("—");
        });

        // Add click event handler for collapse buttons
        clusters.selectAll(".collapse-button, .collapse-icon")
        .on("click", function(event, [clusterName, clusterData]) {
            event.stopPropagation(); // Prevent event from bubbling to SVG
            // Collapse the container
            collapsedContainers.add(clusterName);
            
            // Track this container and operation for centering after reload
            lastModifiedContainer = clusterName;
            lastOperation = "collapse";
            reloadGraph();
        });

        
        // Create edges using the path points from Graphviz
        const edges = inner.selectAll(".edge")
        .data(graphvizData.edges)
        .enter()
        .append("g")
        .attr("class", "edge-group");
        
        // Add the actual path
        edges.append("path")
        .attr("class", "edge")
        .attr("d", function(edge) {
            if (edge.path && edge.path.length > 0) {
                    // Create a proper SVG path using Bézier curves
                let d = "";
                const points = edge.path;
                
                    // Start at the first point
                d += `M${points[0].x},${points[0].y}`;
                
                    // Create cubic Bézier curves
                for (let i = 1; i < points.length - 2; i += 3) {
                    if (i + 2 < points.length) {
                        d += ` C${points[i].x},${points[i].y} ${points[i+1].x},${points[i+1].y} ${points[i+2].x},${points[i+2].y}`;
                    }
                }
                
                    // If we have leftover points, draw a line to the last point
                if (points.length % 3 !== 1) {
                    d += ` L${points[points.length-1].x},${points[points.length-1].y}`;
                }
                
                return d;
            }
            return "";
        })
        .attr("marker-end", "url(#arrowhead_65f5e9a0-380e-4bcb-8217-2a9193d7ba29)");
        
        // Add dimension labels on edges
        edges.each(function(edge) {
            const edgeGroup = d3.select(this);
            const path = edgeGroup.select("path").node();
            
            if (!path) return;
            
            // Find the source node data
            if (!adj_list[edge.source] && !adj_list_collapsed_nodes[edge.source]) return;
            
            // Find the edge data with matching target
            let sourceNodeData = adj_list_collapsed_nodes[edge.source];
            if (!sourceNodeData) {
                sourceNodeData = adj_list[edge.source];
            }
            const edgeData = sourceNodeData.edges.find(e => e.target === edge.target);
            if (!edgeData || !edgeData.dims) return;
            
            // Calculate position for label - near the start of the edge
            // Get a point that's 20% along the path
            const pathLength = path.getTotalLength();
            const pointAtStart = path.getPointAtLength(pathLength * 0.5);
            
            // Get path direction at that point for text rotation
            const pointBefore = path.getPointAtLength(pathLength * 0.4);
            const pointAfter = path.getPointAtLength(pathLength * 0.6);
            // const angle = Math.atan2(pointAfter.y - pointBefore.y, pointAfter.x - pointBefore.x) * 180 / Math.PI;
            const angle = 0.0;
            
            // Create a background for the text
            const labelGroup = edgeGroup.append("g")
            .attr("class", "edge-label")
            .attr("transform", `translate(${pointAtStart.x}, ${pointAtStart.y})`);
            
            const text = labelGroup.append("text")
            .attr("text-anchor", "middle")
            .attr("dominant-baseline", "central")
            .style("font-size", "5px")
            .text(edgeData.dims)
            .attr("transform", `rotate(${angle})`)
            .attr("stroke", "#f3f3eb")
            .attr("stroke-width", "5px")
            .attr("paint-order", "stroke")
            .attr("stroke-linejoin", "round");

            // Add background rectangle
            const bbox = text.node().getBBox();
            labelGroup.insert("rect", "text")
            .attr("x", bbox.x - 2)
            .attr("y", bbox.y - 2)
            .attr("rx", 4)
            .attr("ry", 4)
            .attr("width", bbox.width + 4)
            .attr("height", bbox.height + 4)
            .attr("fill", "rgb(243, 243, 235) ")
            .attr("fill-opacity", 1.0)
            .attr("transform", `rotate(${angle})`);
        });
        

        // Add hover effects to edge paths
        edges.each(function() {
            const edgeGroup = d3.select(this);
            const path = edgeGroup.select("path");
            const labelGroup = edgeGroup.select(".edge-label");
            const labelText = labelGroup.select("text");
            const labelBg = labelGroup.select("rect");
            const originalStrokeWidth = path.attr("stroke-width") || 1;
            const originalFontSize = labelText.style("font-size") || "5px";
            const originalParent = edgeGroup.node().parentNode;
            const originalPosition = Array.from(originalParent.childNodes).indexOf(edgeGroup.node());
            
            function highlightEdge() {
                // Move to end of parent's children list to appear on top
                originalParent.appendChild(edgeGroup.node());
                
                // Make edge thicker
                path.attr("stroke-width", parseFloat(originalStrokeWidth) * 1.4);
                
                // Make label text larger
                labelText.style("font-size", "7px");
                    
                // Adjust the background rectangle for the label
                const bbox = labelText.node().getBBox();
                labelBg.attr("x", bbox.x - 2)
                    .attr("y", bbox.y - 2)
                    .attr("width", bbox.width + 4)
                    .attr("height", bbox.height + 4);
            }
            
            function resetEdge() {
                // Restore original position in parent's children list
                const currentIndex = Array.from(originalParent.childNodes).indexOf(edgeGroup.node());
                if (currentIndex !== originalPosition) {
                    // Insert back at original position
                    const referenceNode = originalParent.childNodes[originalPosition] || null;
                    originalParent.insertBefore(edgeGroup.node(), referenceNode);
                }
                
                // Restore original edge thickness
                path.attr("stroke-width", originalStrokeWidth);
                
                // Restore original label text size
                labelText.style("font-size", originalFontSize);
                    
                // Restore original background rectangle size
                const bbox = labelText.node().getBBox();
                labelBg.attr("x", bbox.x - 2)
                    .attr("y", bbox.y - 2)
                    .attr("width", bbox.width + 4)
                    .attr("height", bbox.height + 4);
            }
            
            // Add hover events to the path
            path.on("mouseenter", highlightEdge)
                .on("mouseleave", resetEdge);
            
            // Add hover events to label group (both text and background)
            if (labelGroup.node()) {
                labelText.on("mouseenter", highlightEdge)
                    .on("mouseleave", resetEdge);
                    
                labelBg.on("mouseenter", highlightEdge)
                    .on("mouseleave", resetEdge);
            }
        });


        // Create nodes using positions from Graphviz
        const nodes = inner.selectAll(".node")
            .data(Object.entries(graphvizData.nodes))
            .enter()
            .append("g")
            // In the createGraph function, when you're creating nodes, modify the class assignment:
            .attr("class", function([nodeName, nodeData]) {
                let classes = "node";
                if ((adj_list[nodeName] && adj_list[nodeName].failed) || containersWithFailedNodes.has(nodeName)) classes += " error";
                if ((adj_list[nodeName] && adj_list[nodeName].node_type === "Input")) classes += " input";
                else if ((adj_list[nodeName] && adj_list[nodeName].node_type === "Module") || (collapsedContainers.has(nodeName))) classes += " module";
                else if ((adj_list[nodeName] && adj_list[nodeName].node_type === "Output")) classes += " output";
                else if ((adj_list[nodeName] && adj_list[nodeName].node_type === "Operation")) classes += " operation";
                else if ((adj_list[nodeName] && adj_list[nodeName].node_type === "Constant")) classes += " constant";
                return classes;
            })
            .attr("transform", function([nodeName, nodeData]) {
                return `translate(${nodeData.position.x}, ${nodeData.position.y})`;
            });
        
        // Filter nodes for click event - don't include input or output nodes
        inner.selectAll(".node")
            .filter(function([nodeName]) {
                return !(((adj_list[nodeName] && adj_list[nodeName].node_type === "Output")) || ((adj_list[nodeName] && adj_list[nodeName].node_type === "Input")) || ((adj_list[nodeName] && adj_list[nodeName].node_type === "Constant"))) && !collapsedContainers.has(nodeName)
            })
            .on("click", showPopup);

        
        // Add rectangles for nodes
        nodes.append("rect")
        .attr("width", function([nodeName, nodeData]) { return nodeData.width; })
        .attr("height", function([nodeName, nodeData]) { return nodeData.height; })
        .attr("x", function([nodeName, nodeData]) { return -nodeData.width / 2; })
        .attr("y", function([nodeName, nodeData]) { return -nodeData.height / 2; })
        .attr("class", function([nodeName]) { 
            if (adj_list[nodeName] && adj_list[nodeName].node_type === "Input") return "input";
            if (adj_list[nodeName] && adj_list[nodeName].node_type === "Output") return "output";
            if (adj_list[nodeName] && adj_list[nodeName].node_type === "Constant") return "constant";
            if ((adj_list[nodeName] && adj_list[nodeName].failed) || containersWithFailedNodes.has(nodeName)) return "failed";
            return ((adj_list[nodeName] && adj_list[nodeName].node_type === "Module") || (collapsedContainers.has(nodeName))) ? "module" : "operation";
        });
        
        // Add labels for nodes
        nodes.append("text")
        .attr("class", "node-label")
        .text(function([nodeName]) { return graph_node_name_to_without_suffix[nodeName] || nodeName; })
        .attr("text-anchor", "middle")
        .attr("dominant-baseline", "central");
        
        // Add type indicators
        nodes.filter(function([nodeName]) {
            return !(((adj_list[nodeName] && adj_list[nodeName].node_type === "Output")) || ((adj_list[nodeName] && adj_list[nodeName].node_type === "Input")) || ((adj_list[nodeName] && adj_list[nodeName].node_type === "Constant")));
        }).append("text")
        .attr("class", "type-indicator")
        .attr("text-anchor", "end")
        .attr("dominant-baseline", "hanging")
        .attr("transform", function([nodeName, nodeData]) {
            return `translate(${nodeData.width / 2 - 3}, ${-nodeData.height / 2 + 3})`;
        })
        .text(function([nodeName]) {
            return (adj_list[nodeName] && adj_list[nodeName].node_type === "Module") || (collapsedContainers.has(nodeName)) ? "Module" : "Tensor Op";
        });

        // Add collapse/expand buttons to collapsed container nodes
        inner.selectAll(".node")
        .filter(function([nodeName]) {
            return collapsedContainers.has(nodeName);
        })
        .each(function([nodeName, nodeData]) {
            const nodeGroup = d3.select(this);
            const width = nodeData.width;
            const height = nodeData.height;        
            // Add collapse/expand button (with "+" since it's collapsed)
            nodeGroup.append("circle")
            .attr("class", "collapse-button")
            .attr("cx", -width/2 + 8)
            .attr("cy", -height/2 + 8)
            .attr("r", 6)
            .attr("fill", "#fff")
            .attr("stroke", "#333")
            .attr("stroke-width", 1)
            .attr("cursor", "pointer");
        
            // Add "+" symbol
            nodeGroup.append("text")
            .attr("class", "collapse-icon")
            .attr("x", -width/2 + 8)
            .attr("y", -height/2 + 8) 
            .attr("text-anchor", "middle")
            .attr("dominant-baseline", "middle")
            .attr("font-size", "10px")
            .attr("font-weight", "bold")
            .attr("cursor", "pointer")
            .text("+");
            
            // Add click handler using the existing reloadGraph function
            nodeGroup.selectAll(".collapse-button, .collapse-icon")
            .on("click", function(event) {
                event.stopPropagation();
                
                // Always expanding when clicked (since these are collapsed containers)
                collapsedContainers.delete(nodeName);

                // Track this container and operation for centering after reload
                lastModifiedContainer = nodeName;
                lastOperation = "expand";
                
                // Use the existing reloadGraph function
                reloadGraph();
            });
        });    

        // Add hover effects
        nodes.each(function() {
            const nodeGroup = d3.select(this);
            const rect = nodeGroup.select("rect");
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            let isHovered = false;
            
            nodeGroup.on("mouseenter", function() {
                if (!isHovered) {
                    isHovered = true;
                    const currentColor = d3.color(rect.style("fill") || originalColor);
                    rect.style("fill", currentColor.brighter(0.4));
                }
            })
            .on("mouseleave", function() {
                isHovered = false;
                rect.style("fill", originalColor);
            });
        });
        
        
        // Implement popup functionality
        function showPopup(event, [nodeName]) {
            event.stopPropagation();
            
            const popup = d3.select("#popup_65f5e9a0-380e-4bcb-8217-2a9193d7ba29");
            const clickedElement = d3.select(this).select("rect").node();
            const moduleTemplate = d3.select("#module-template_65f5e9a0-380e-4bcb-8217-2a9193d7ba29");
            const tensorOpTemplate = d3.select("#tensor-op-template_65f5e9a0-380e-4bcb-8217-2a9193d7ba29");
            const noInfoMessage = d3.select("#no-info_65f5e9a0-380e-4bcb-8217-2a9193d7ba29");
            
            const nodeData = adj_list[nodeName];
            
            d3.select("#popup-title_65f5e9a0-380e-4bcb-8217-2a9193d7ba29").text(graph_node_name_to_without_suffix[nodeName] || nodeName);
            
            // d3.select("#input-dims_65f5e9a0-380e-4bcb-8217-2a9193d7ba29").text(nodeData.input_dims);
            // d3.select("#output-dims_65f5e9a0-380e-4bcb-8217-2a9193d7ba29").text(nodeData.output_dims);
            
            moduleTemplate.style("display", "none");
            tensorOpTemplate.style("display", "none");
            noInfoMessage.style("display", "none");
            
            if (((adj_list[nodeName] && adj_list[nodeName].node_type === "Module")) && (module_info[nodeName] || (module_name_to_base_name[nodeName] && module_info[module_name_to_base_name[nodeName]]))) {
                const info = module_info[nodeName] ? module_info[nodeName] : module_info[module_name_to_base_name[nodeName]];
                moduleTemplate.style("display", "block").classed("content-shown", true);
                
                d3.select("#module-type_65f5e9a0-380e-4bcb-8217-2a9193d7ba29").text(info.type || 'N/A');
                d3.select("#module-attributes_65f5e9a0-380e-4bcb-8217-2a9193d7ba29").text(JSON.stringify(info.attributes, null, 2));
                d3.select("#module-parameters_65f5e9a0-380e-4bcb-8217-2a9193d7ba29").text(JSON.stringify(info.parameters, null, 2));
                d3.select("#module-extra-repr_65f5e9a0-380e-4bcb-8217-2a9193d7ba29").text(info.extra_repr || 'N/A');
            } else if (!(adj_list[nodeName] && adj_list[nodeName].node_type === "Input") && func_info_map[nodeName]) {
                const info = func_info_map[nodeName];
                tensorOpTemplate.style("display", "block").classed("content-shown", true);
                
                d3.select("#tensor-parameters_65f5e9a0-380e-4bcb-8217-2a9193d7ba29").text(JSON.stringify(info, null, 2));
            } else {
                noInfoMessage.style("display", "block").classed("content-shown", true);
            }
            
            const boundingBox = clickedElement.getBoundingClientRect();
            const containerRect = d3.select("#graph-container_65f5e9a0-380e-4bcb-8217-2a9193d7ba29").node().getBoundingClientRect();
            
            const x = boundingBox.left + boundingBox.width / 2 - containerRect.left;
            const y = boundingBox.top + boundingBox.height / 2 - containerRect.top;
            
            popup.style("left", `${x}px`)
            .style("top", `${y}px`)
            .style("display", "flex");
            d3.select("#popup-content_65f5e9a0-380e-4bcb-8217-2a9193d7ba29").node().scrollTop = 0;
        }
        
        function closePopup() {
            d3.select("#popup_65f5e9a0-380e-4bcb-8217-2a9193d7ba29").style("display", "none");
        }
        
        d3.select(".popup-close").on("click", closePopup);
        svg.on("click", closePopup);
        
        // Calculate view box to ensure the graph is properly scaled and centered
        const findErrorNode = (nodes) => {
            for (const [nodeName] of nodes) {
                if (adj_list[nodeName] && adj_list[nodeName].failed) {
                    return nodeName;
                }
            }
            return null;
        };
        
        const errorNode = findErrorNode(Object.entries(graphvizData.nodes));
        
        // Initial zoom setup
        const svgNode = svg.node();
        const width = svgNode.clientWidth || 800; // Fallback if clientWidth is 0
        const height = svgNode.clientHeight || 600; // Fallback if clientHeight is 0
        
        if (lastModifiedContainer && (Object.keys(graphvizData.nodes).includes(lastModifiedContainer) || 
            Object.keys(graphvizData.clusters).includes(lastModifiedContainer))) {
            
            // Center on the container that was just expanded or collapsed
            let targetCoverage = lastOperation === "expand" ? EXPAND_TARGET_COVERAGE : COLLAPSE_TARGET_COVERAGE;
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            let targetPos;
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            if (Object.keys(graphvizData.nodes).includes(lastModifiedContainer)) {
                // It's a node (collapsed container)
                targetNode = graphvizData.nodes[lastModifiedContainer];
                targetPos = { x: targetNode.position.x, y: targetNode.position.y };
                containerWidth = targetNode.width;
                containerHeight = targetNode.height;
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                // It's a cluster (expanded container)
                const clusterData = graphvizData.clusters[lastModifiedContainer];
                const [x1, y1, x2, y2] = clusterData.bb.split(",").map(Number);
                targetPos = { x: (x1 + x2) / 2, y: (y1 + y2) / 2 };
                containerWidth = x2 - x1;
                containerHeight = y2 - y1;
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            if (targetPos && containerWidth && containerHeight) {
                // Calculate scale to make container occupy the target percentage of viewport
                const widthScale = (width * targetCoverage) / containerWidth;
                const heightScale = (height * targetCoverage) / containerHeight;
                // Use the smaller scale to ensure container fits in viewport
                const targetScale = Math.min(widthScale, heightScale);
                
                const translate = [
                    width / 2 - targetPos.x * targetScale,
                    height / 2 - targetPos.y * targetScale
                ];
                
                svg.call(zoom.transform, d3.zoomIdentity
                    .translate(translate[0], translate[1])
                    .scale(targetScale));
                
                // Apply highlight effect to the target container
                if (Object.keys(graphvizData.nodes).includes(lastModifiedContainer)) {
                    // Highlight the node (collapsed container)
                    const targetElement = inner.selectAll(".node")
                        .filter(([nodeName]) => nodeName === lastModifiedContainer);
                        
                    if (!targetElement.empty()) {
                        targetElement.classed("highlight-node", true);
                        
                        // Remove the highlight class after animation completes
                        setTimeout(() => {
                            targetElement.classed("highlight-node", false);
                        }, 2000); // Match the animation duration
                    }
                } else if (Object.keys(graphvizData.clusters).includes(lastModifiedContainer)) {
                    // Highlight the cluster (expanded container)
                    const targetElement = inner.selectAll(".boundary-box")
                        .filter(function([clusterName]) {
                            return clusterName === lastModifiedContainer;
                        });
                        
                    if (!targetElement.empty()) {
                        targetElement.classed("highlight-cluster", true);
                        
                        // Remove the highlight class after animation completes
                        setTimeout(() => {
                            targetElement.classed("highlight-cluster", false);
                        }, 2000); // Match the animation duration
                    }
                }
                
                // Reset tracking variables after applying the transformation
                lastModifiedContainer = null;
                lastOperation = null;
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        } else if (errorNode) {
            // Focus on the error node if there is one
            const nodeData = graphvizData.nodes[errorNode];
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                const scale = 2; // Zoom in on error node
                const translate = [
                    width / 2 - nodeData.position.x * scale,
                    height / 2 - nodeData.position.y * scale
                    ];
                
                svg.call(zoom.transform, d3.zoomIdentity
                    .translate(translate[0], translate[1])
                    .scale(scale));
            }
        } else {
            // Show the entire graph
            // Find the bounds of the graph
            let minX = Infinity, minY = Infinity;
            let maxX = -Infinity, maxY = -Infinity;
            
            Object.values(graphvizData.nodes).forEach(node => {
                minX = Math.min(minX, node.position.x - node.width / 2);
                minY = Math.min(minY, node.position.y - node.height / 2);
                maxX = Math.max(maxX, node.position.x + node.width / 2);
                maxY = Math.max(maxY, node.position.y + node.height / 2);
            });
            
            // Also consider cluster bounding boxes
            Object.values(graphvizData.clusters).forEach(cluster => {
                const [x1, y1, x2, y2] = cluster.bb.split(",").map(Number);
                minX = Math.min(minX, x1);
                minY = Math.min(minY, y1);
                maxX = Math.max(maxX, x2);
                maxY = Math.max(maxY, y2);
            });
            
            const graphWidth = maxX - minX;
            const graphHeight = maxY - minY;
            
            // Determine scale to fit the entire graph with some padding
            const scale = Math.min(width / graphWidth, height / graphHeight) * 0.9;
            
            // Center the graph
            const translate = [
                width / 2 - (minX + graphWidth / 2) * scale,
                height / 2 - (minY + graphHeight / 2) * scale
                ];
            
            svg.call(zoom.transform, d3.zoomIdentity
                .translate(translate[0], translate[1])
                .scale(scale));
        }
            
    }
})();
        </script>

        <div class="container">
            <div class="left-column">

<div class="code-box" id="codeBox">
    <div class="code-box-header">Sample code (Not editable)</div>
    <pre><code class="language-python">
import torch
from torchvision.models import squeezenet1_1
from torchvista import trace_model

import timm

model = timm.create_model('mobilevit_s', pretrained=True)
example_input = torch.randn(1, 3, 256, 256)

trace_model(model, example_input)
</code></pre>
</div>

<div class="error-box" id="errorBox">
    <div class="error-box-header">Error Output (if any)</div>

</div>

            </div>
    
            <div class="graph-view" id="graphContainer" style="height: 100%">
                <div class="graph-box-header">Visualized Interactive Graph</div>
                <div id="graph-container_65f5e9a0-380e-4bcb-8217-2a9193d7ba29" style="height: 97%; overflow: auto; position: relative;">
                    <div id="graph_65f5e9a0-380e-4bcb-8217-2a9193d7ba29" style="height: 100%"></div>
                    <div id="popup_65f5e9a0-380e-4bcb-8217-2a9193d7ba29" class="popup" style="display: none;">
                        <div class="popup-header">
                            <h3 id="popup-title_65f5e9a0-380e-4bcb-8217-2a9193d7ba29"></h3>
                            <span class="popup-close">✕</span>
                        </div>
                        <div id="popup-content_65f5e9a0-380e-4bcb-8217-2a9193d7ba29" class="popup-content">
                            <div id="common-info_65f5e9a0-380e-4bcb-8217-2a9193d7ba29">
                            </div>
                            <div id="module-template_65f5e9a0-380e-4bcb-8217-2a9193d7ba29" style="display:none;">
                                <p><strong>Type:</strong> <span id="module-type_65f5e9a0-380e-4bcb-8217-2a9193d7ba29"></span></p>
                                <p><strong>Attributes:</strong></p>
                                <pre id="module-attributes_65f5e9a0-380e-4bcb-8217-2a9193d7ba29"></pre>
                                <p><strong>Parameters:</strong></p>
                                <pre id="module-parameters_65f5e9a0-380e-4bcb-8217-2a9193d7ba29"></pre>
                                <p><strong>Extra Repr:</strong></p>
                                <pre id="module-extra-repr_65f5e9a0-380e-4bcb-8217-2a9193d7ba29"></pre>
                            </div>
                            <div id="tensor-op-template_65f5e9a0-380e-4bcb-8217-2a9193d7ba29" style="display:none;">
                                <p><strong>Parameters:</strong></p>
                                <pre id="tensor-parameters_65f5e9a0-380e-4bcb-8217-2a9193d7ba29"></pre>
                            </div>
                            <p id="no-info_65f5e9a0-380e-4bcb-8217-2a9193d7ba29" style="display:none;">No further information available for this node.</p>
                        </div>
                    </div>
                    <div id="popup-content_65f5e9a0-380e-4bcb-8217-2a9193d7ba29"></div>
                </div>
            </div>
        </div>
    </body>
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